izzako/sundanese-pixelgpt huggingface dataset:
"Raw data" is a relative term; data processing commonly occurs by stages, and the "processed data" from one stage may be considered the "raw data" of the next
Mechanical computing devices are classified according to the méans by which they represent data
An analog computer represents a datum as a voltage, distance, position, or other physical quantity
A digital computer represents a datum as a sequence of symbols drawn from a fixed alphabet
The most common digital computers use a binary alphabet, that is, an alphabet of two characters, typically denoted "0" and "1"
More familiar representations, such as numbers and letters, are then constructed from the binary alphabet
Some special forms of data are distinguished
A computer program is a collection of data which can be interpreted as instructions
Most computer languages maké a distinction between programs and the other data on which programs operate, but in some languages, notably Lisp and similar languages, programs are essentially indistinguishable from other data
It is also useful to distinguish metadata, that is, a description of other data
The prototypical example of metadata is the library catalog, which is a description of the contents of books
Hartina data jeung informasi
Data on its own has no méaning, only when interpreted by some kind of data processing system does it take on méaning and become information
Péople or computers can find patterns in data to perceive information, and information can be used to enhance knowledge
Since knowledge is prerequisite to wisdom, we always want more data and information
But, as modérn societies verge on information overload, we especially need better ways to find patterns
"This article (or an earlier version of it) contains material from FOLDOC, used with permission."
Milennium: 2nd millennium &lt;br&gt;Centuries: 18th century - 19th century - 20th century&lt;/br&gt; Timelines: 18th century - 19th century - 20th century &lt;br&gt;State leaders: 18th century - 19th century - 20th century&lt;/br&gt; Decades: 1800s 1810s 1820s 1830s 1840s 1850s 1860s 1870s 1880s 1890s &lt;br&gt;Categories: &lt;/br&gt; Salaku catetan waktu nu kaliwat, abad ka-19 nyaéta abad taun-taunna antara 1801-1900
Abad ka-19 mindeng pisan ditujul ku sajarahwan salaku "mangsa isme", nu nyirikeun lobana isme nu tumuwuh dina mangsa harita
Taya deui abad nu ngalaman parobahan sosial sacara masif kawas taun 1800-an
Ari abad ka-20 mah abadna pulitik jeung élmu, abad ka-19 mah abadna masarakat
For the first time, the rights of the workers and common man were being questioned
Rarely in previous times did such a massive movement across Europe, into the Americas, and even parts of Asia occur
1848 alone felt the effects of the new idéas as Européan cities from Paris to Vienna were in uprise
The 19th century was a contrast from old to new, the old monarchies and feudal systems to the new capitalist world and democracy
The 19th century was the opening stage for the modérn world
Dina statistik, titik estimasi ngalibatkeun sampel data keur ngitung hiji nilai anu mangrupa "best guess" tina parameter populasi (fixed atawa random) nu teu dipikanyaho
Dina kaayaan nu leuwih formal, mangrupa pamakéan titik estimasi tina data
Hiji sampel mangrupa bagian tina hiji populasi anu ditalungtik sabenerna
Dina elmu pangaweruh praktis, urang merlukeun yén sampel dipilih ku rupa-rupa cara supaya méré gambaran bias tina populasi
Lamun kaputusan statistik bakal digunakeun, mangka kudu aya jalan keur nganyahokeun probibiliti dina milih unggal sampel
Lamun probabiliti tina sampel nu béda sakabéhna sarua, upamana, métodena disebut sampling acak basajan
"Tempo ogé": Sampling (statistics)
Dina statistik, prinsip likelihood mangrupa prinsip kontroversi tina inferensi statistik, nu mana ieu prinsip nempokeun sakabéh informasi dina sampel nu aya dina fungsi likelihood
Fungsi likelihood nyaéta distribusi probabilitas kondisional dianggap minangka fungsi argumen, nu mana fungsi mimitina dianggap angger
Conto, anggap hiji modél nempokeun fungsi dénsitas probabilitas (dina kasus diskrit, fungsi probabilitas massa) nu dicokot tina variabel acak "X" minangka fungsi paraméter θ. Saterusna keur niley husus "x", fungsi L(θ) = P("X" = "x" | θ) nyaéta fungsi likelihood θ. Dua fungsi likelihodd dianggap sarua lamun mangrupa hasil kali dua skalar; prinsip likelihood nyebutkeun yén sakabéh informasi nu pakait jeung kasimpulan ngeunaan niley
nu pakait jeung kasimpulan ngeunaan niley θ bakal kapanggih dina kelas nu sarua
Anggap Mangka observasi yén "X" = 3 nyababkeun fungsi likelihood formula_1 sarta observasi yén "Y" = 5 nyababkeun fungsi likelihood formula_2 Ieu sarua sabab hasil kali skala
Prinsip likelihood nyebutkeun yén kaputusan nu digambarkuen ngadeukeutan nilai θ kudu sarua dina dua kasus eta
béda antara observing "X" = 3 jeung observing "Y" = 5 ngan ukur dina desain percobaan: dina hiji kasus, hiji mibanda kaputusan jéntré keur nyoba lima kali; dinu séjénna, nyoba tilu sukses nu di-observasi
"Hasil"-na sarua dina dua kasus eta
Sanajan kitu, prinsip likelihood kadangkala netepkeun yen: "Kaputusan kudu gumantung ngan kana hasil percobaan, sarta lain kana desain percobaan."
Konsép nu pakait nyaéta hukum likelihood, notasi nu dilegaan keur ngadukung hiji nilai paraméter atawa hipotesa séjénna sarua jeung rasio "likelihood"-na
Upamana, P("X" | "a")/P("X" | "b") nyaéta tingkat nu mana data "X" ngadukung nilai paraméter atawa hipotesa "a" ka "b"
Lamun rasio ieu sarua jeung 1, bukti kajadian teu béda, jeung lamun leuwih atawa kurang ti 1, kajadian "a" ngadukung "b" atawa sabalikna
Kombinasi prinsip likelihood nu ngagunakeun hukum likelihood ngahasilkeun konsekuensi yén nilai paraméter nu dimaksimalkeun ku fungsi likelihood nyaéta nilai nu leuwih didukung ku kajadian
Dumasar kana hal ieu ilahar dipaké metoda maksimum likelihood
Prinsip likelihood mimiti kapanggih dina sababaraha tulisan dina taun 1962 (Barnard et al., Birnbaum, and Savage et al
), tapi alesan keur prinsip nu sarua, teu maké ngaran, tur maké prinsip ieu dina sababaraha hal, bisa kapanggih dina sababaraha pagawéan R.A. Fisher dina taun 1920
Hukum likelihood diwanohkeun ku I. Hacking (1965)
Nu leuwih anyar dina prinsip likelihood minangka prinsip nu ilahar dipaké dina inferensi mimiti diwanohkeun ku Anthony W.F
Prinsip likelihood geus dipaké dina elmu filsuf ku R. Royall
Alesan keur jeung lawan prinsip likelihood
Prinsip likelihood teu ditarima sacara universal
Sababaraha métodeu konvensional masih dipaké dina statistik, contona tes signifikan, nu teu konsisten dina prinsip likelihood
Sacara jelas katempo yén tes rasio-likelihood dumasar kana ieu prinsip
Urang tempo sacara singget sababaraha alesan keur jeung lawan prinsip likelihood
Alesan dina prinsip likelihood
Tina panempo Bayesian, prinsip likelihood aya dina saluareun teori Bayes
Hiji observasi "A" asup kana rumus, formula_3 ngan maké fungsi likelihood, formula_4
Sacara umum, observasi datang kana aturan ngaliwatan fungsi likelihood jeung ngan maké fungsi likelihood: taya mekanisme séjén nu diperlukeun
Alesan lawan prinsip likelihood
Prinsip likelihood nyebutkeun yén unggal kajadian nu teu jadi teu mangaruhan dina hiji kajadian, lamun aya teu bener-bener kajadian tapi mangaruhan kana hiji kajadian, mangka aya sababaraha informasi nu teu kawengku dina fungsi likelihood
Sanajan kitu, aya nu teu bener-bener kajadian dipaké dina sababaraha hal nu ilahar dina métodeu statistik
Upamana hasil tina tes signifikan gumantung kana hasil ekstrim atawa leuwih ekstrim tina probabilitas tinimbang tina observasi
Mangka keur ngalegaan ieu métodeu , prinsip likelihood teu dipaké
Prinsip likelihood keur sababaraha urang nembongkeung hasil nu ampir "paradoks"
Conto nu ilahar dipaké nyaéta opsi ngeureunkeun masalah
Anggap kuring ngalungkeun koin sapuluh kali tur meunang tujuh kali sirah
Mangka anjeun bakal ngira-ngira ngeunaan probabilitas éta sirah
Anggap ayeuna kuring nyebutkeun yén kuring ngalungkeun koin nu katempo 7 sirah sarta ngalungkeunna sapuluh kali
Naha anjeun nyieun kasimpulan nu béda
Fungsi likelihood dina dua kases eta, nyaéta sarua jeung formula_5 Dumasar kana prinsip likelihood, kasimpulan kudu sarua dina kasus nu béda
Tapi ieu ampir sarua jeung kajadian nguseup: bisa dianggap cukup keur "alesan nu ngahasilkeun kasimpulan" tina kasus ngaleungkeun koin tadi
Aya sababaraha conto paradoks séjén dina kahirupan dianggap lawan kana prinsip likelihood
Statistik, saperti dina sakabéh widang matematik teu nyimpulkeun hiji hal nu tina tina nu euweuh
Dina usaha néangan kasimpulan nu bener ngeunaan populasi statistik, ilaharna diperlukeun keur nyieun sababaraha anggapan mimiti
Ieu kudu dijieun sacara "hati-hati", sabab kasalahan dina nyieun anggapan baris ngakibatkeun kana kasimpulan nu kacida salahna
Anggapan nu ilahar dipaké dia statistik nyaéta: Rujukan
Manusa (Homo sapiens) mangrupa hiji spésiés ti "Kera Gedé" ("Great Ape") jeung hiji-hijina spésiés nu salamet ti marga "Homo"
Spésiés ieu biasa ogé disebut "jelema", "jalma", "kamanusaan", atawa "umat manusa"
Manusa punjul ku kapinteranna jeung kabisana ngagunakeun basa
"Artikel utama: Évolusi manusa" Dulur évolusionér nu pangdeukeutna ka manusa nyaéta dua spésiés simpanse "Pan troglodytes" (""common chimp") jeung "Pan paniscus" ("pygmy chimp"" atawa "Bonobo"), sarta ka nu darajat duduluranana leuwih handap hominoid séjénna kayaning orangutan jeung gorilla
Penting dicatet, yén manusa ukur babagi hiji "common ancestor" jeung nu disarebut tadi, henteu diturunkeun sacara langsung ti maranéhna
Para ahli biologi geus ngabandingkeun runtuyan pasangan basa DNA antara manusa jeung simpanseu, sarta ngira-ngira béda génétikna ukur 5%
Hasil ngira-ngira nunjukkeun yén karuhun manusa misah ti simpanseu 5 jutaan taun ka tukang, sedengkeun ti gorilla watara 8 juta taun ka tukang
However, recent news reports of a hominid skull approximately 7 million yéars old alréady showing a divergence from the ape linéage strongly suggests an éarlier divergence
Some scientists argue that bonobos, chimpanzees and, possibly, gorillas should be lumped into the genus "Homo", but this is currently a minority opinion
Loba golongan agamis anu kabeuratan jeung kontroversi ngeunaan tiori évolusi manusa ti hiji "common ancestor" jeung hominoid séjénna
Tempo kréasionisme jeung pamanggih ti évolusi pikeun jihat sawangan nu patojaiyah
Awak manusa didadarkeun na kumpulan artikel anatomi manusa
Manusa mibanda loba pisan range of variability dina ciri pisik jeung karakter lainna
The evolution of "Homo sapiens" is characterized by a number of important trends: How these trends are related, in what ways they have been adaptive, and what their role is in the evolution of complex social organization and culture, are matters of ongoing debate among physical anthropologists
Although body size is highly heritable, it is also significantly influenced by environmental and cultural factors such as diet
The méan height of an American adult female is 162 cm (64 in) and the méan weight is 62 kg (137 lb)
Males are typically larger: 175 cm (69 in) and 78 kilograms (172 lb)
Humans vary substantially around these méans, and the méans themselves have varied depending on locality and historical factors
Human children, typically weighing 3-4 kilograms (6-9 pounds) and 50-60 centimetres (20-24 inches) in height, are born after a nine-month gestation period
Helpless at birth, they continue to grow for some yéars, typically réaching sexual maturity at around 12–15 yéars of age
Boys continue growing for some time after this, often only réaching their maximum height around the age of 18
Human life expectancy at birth is approaching 80 yéars in wéalthy nations, with the assistance of science and technology
The number of centenarians in the world was estimated at about 50,000 in 2003
The maximum human life span is thought to be about 120 yéars
See also human physical appearance
Manusa nganggap manéhna salaku organisme pangpinterna di karajaan sato
Manusa mibanda nisbah otak nepika ka beurat awak pangbadagna ti sakabéh sato badag (lumba-lumba kadua; hiu pangluhurna di dunya lauk; sedengkeun octopus pangluhurna di dunya invertebrata)
Najan ieu teu mangrupa ukuran absolut (inasmuch as a minimum brain-mass is necessary for certain "housekeeping" functions), nisbah massa otak ka massa awak mémang méré cicirén nu hadé pikeun kapinteran rélatif (Carl Sagan, The Dragons of Eden, 38)
Kamampuhan manusa kana abstraksi teu paralél na karajaan sato
Hasil-hasil uji geus nunjukkeun yén simpanse déwasa kurang leuwih mibanda kamampuhan abstraksi nu sarua jeung budak umur opat taun
Pattern recognition is another aréa for which human beings are mentally well-suited
Mikir, IQ, Memory, Invention, Élmu, Filosofi, Pangaweruh, Atikan, Consciousness Ciri émosional
Émosi, Tresna, Cua, Bungah, jsb
Agama miara yén di sagigireun sifat fisik jeung méntalna, umat manusa ogé mibanda sifat spiritual; loba nu yakin yén ayana sifat spiritual ieu nu ngabédakeun umat manusa ti mahluk séjén
Sabalikna, kaom atéis yakin yén manusa teu mibanda aspék spiritual, antukna teu bina ti nu séjén
Roh, Conscience, Agama, Moralitas, Prayer, Worship, etc
Habitat asli nalika manusa ngalaman évolusi nyéta di sabana Afrika (tempo Vagina gentium, Environment of Evolutionary Adaptedness)
Téhnologi nu népa sacara kultural geus ngajalanan manusa pikeun bumén-bumén di sadaya buana sarta nyaluyukeun manéh jeung sadaya iklim
Within the last few decades, humans have been able to temporarily inhabit Antarctica, the océan depths, and outer space, although permanent habitation of these three environments is not yet possible
Humans, with a population of about six billion, are one of the most numerous mammals on éarth
Most humans (61%) live in the Asian region
The vast majority of the remainder live in the Amerika (14%), Afrika (13%) jeung Eropa (12%), with only 0.3% in Australia
See list of countries by population and list of countries by population density
Being primates, humans' original life style is hunting/gathering, which is adapted to the savannah where they evolved
Other human life styles are nomadism (often linked to animal herding) and permanent settlements made possible by the development of agriculture
Humans have a gréat capacity for altering their habitats by various methods, such as agriculture, irrigation, urban planning and construction, and activities accessory to those, such as transportation and manufacturing goods
Permanent human settlements are dependent on proximity to water and, depending on the lifestyle, other natural resources such as fertile land for growing crops and grazing livestock or, séasonally by populations of prey
With the advent of large-scale trade and transportation infrastructure, immediate proximity to these resources has become less necessary, and in many places these factors are no longer the driving force behind growth and decline of population
A sizable minority - around 2.5 of a total of 6.3 billion péople - live in urban surroundings
Urbanisation is expected to rise drastically during the 21st century
Problems for humans in cities include various forms of pollution, crime and poverty, especially in inner city and suburban slums
Humans living on Antarctica, under the océan, or in space are part of scientific, military, or industrial expeditions, and habitation of these environments is temporary
Life in space has thus far been temporary living, with up to ten humans in space at a given time (seven on the Space Shuttle, three on Mir) and currently around three in the International Space Station
This is a direct result of humans' vulnerability to ionizing radiation
Prior to 1961, all humans were restricted to the éarth; Yuri Gagarin was the first human to travel into space
At various periods between 1969 and 1974, up to two humans spent varying amounts of time on the Moon
As of yet, residencies or human explorations on other planets have not come to be
Ngabandingkeun "Homo sapiens" jeung spésiés séjén
Humans often consider themselves to be the dominant species on Earth, and the most advanced in intelligence and ability to manage their environment
This belief is especially strong in Western culture, and is based in part on the Biblical Creation story in which Adam is explicitly given dominion over the éarth and all of its créatures
Biologists and scientists in general, though, do not consider "dominant" to be a useful term, because the adaptive value of any trait or complex of traits depends on the niche and is highly mutable
From a scientific standpoint, "Homo sapiens" certainly is among the most generalized species on Earth
Smaller and simpler animals such as bacteria and insects gréatly surpass humans in population size and diversity of species, but few single species occupy as many diverse environments as humans
Many other species, for example, are adapted to specific environments, wheréas humans rely on the use of fire and on tools such as clothing and manufactured shelter, which are themselves often produced and used through complex social interactions
Various attempts have been made to identify a single behavioral characteristic that distinguishes humans from all other animals, e.g
the ability to maké and use tools (building shelter, weaving fabrics for clothing); the ability to alter the environment; language; and the development of complex social relationships and structures
Considered in isolation, however, these differences are not absolute, as ethologists have recorded such behaviors in many species
Apes and even birds, for example, are known to "fish" for insects using blades of grass or twigs, and even to shape the tools for that purpose
For these réasons, the idéa that making and using tools is a defining characteristic of humans is often considered outdated, though of course no other animal uses tools to the same degree or with the same flexibility as "Homo sapiens"
Similarly, other animals often have methods of communication, but the degree to which humans créate and use complex grammar and abstract concepts in language has not been seen in any other species
Chomskian linguistics holds that a distinguishing féature of humans is that they are the only extant species with a language instinct - a genetic predisposition that produces a brain mechanism whose function is to acquire a language by observing those around us
Dolphins may also have this trait as they show dialect
Some anthropologists think that these réadily observable characteristics (tool-making and language) are based on less éasily observable mental processes that might be unique among humans: the ability to think symbolically
That is, humans can think abstractly about concepts and idéas
They can question, use logic, understand mathematical concepts, and so on in ways gréater than other animals are known to do, although several species have demonstrated some abilities in these aréas
In any case, the idéa that these abilities distinguish humans from other species is the basis of the name "Homo sapiens", sometimes translated as "Man the Thinker"
It should be noted, however, that the extinct species of the "Homo" genus (e.g
"Homo neanderthalensis", "Homo erectus") were also adept tool makers and there is some evidence that they may have had linguistic skills
While humans have all these characteristics, from the biological viewpoint the question "What single characteristic distinguishes humans from all other animals?"
is an odd one: it is not a question that is usually asked of cats, dolphins, or song sparrows
Finding other species that shape tools or can use sign language may shed light on human evolution, but it doesn't erase the differences or similarities between humans and other species
Pangaweruh nyaéta harti nu loba watesna kalawan gumantung kana konteksna, sanajan kitu pangaweruh leuwih raket hubunganna jeung sababaraha konsép kayaning harti, émbaran, paréntah, komunikasi, réprésentasi, diajar, jeung stimulus méntal
Pangaweruh béda jeung émbaran (informasi)
Boh pangaweruh atawa émbaran ngandung ungkara nu bener, tapi pangaweruh ngandung harti émbaran lamun mibanda tujuan atawa mngpaat
Para filsuf, ngagambarkeunana pangaweruh nu kaasup émbaran patali jeung inténsionalitas
Studi ngeunaan pangaweruh disebut épistémologi
Tiori statistik ngawengku sababaraha topik, nyaéta: Model statistik sumber data sarta cara ngarumuskeun masalah: Ngarencanakeun panalungtikan statistik pikeun ngukur jeung ngontrol kasalahan amatan (Ing
"observational error"): Summarizing statistical data in conventional forms (also known as statistik deskriptif) Interpreting statistical data is the final objective of all reséarch: Tempo ogé
probability, statistik, daptar jejer statistis
The term statesman is a respectful term used to refer to diplomats, politicians, and other notable figures of state
It is often used in the context of international or foreign affairs, e.g., "a meeting of statesmen."
Foreign ministers are often called statesmen, for example, while more local level officials, such as mayors are not
Whether or not an individual actually "is" a statesman, is generally a matter of opinion, although in some cases there is little controversy
Politicians who are regarded as statesmen are usually old, with long careers
The term most frequently refers to individuals associated with a governmental shift from monarchism to republicanism
Genéally, one can use the word as a euphemism for "politician"
When a politician retires, he is often referred to as a "respected elder statesman" by his supporters
Basa Jérman "(Deutsch, deutsche Sprache)", anggota kelompok kulon basa-basa Jérman, nyaéta salah sahiji basa utama di dunya, ogé mangrupa basa nu boga pamaké asli panglobana di Uni Éropa
Utama dipaké di Jérman, Austria, Liechtenstein, sawaréh utama Swis, Luxembourg, "Südtirol" (Tyrol Kidul) di Itali, Opole Voivodship di Polandia, Canton Wétan di Bélgia, sawaréh Rumania, Alsace jeung sawaréh wewengkon Lorraine di Prancis
Ogé katambah ku sababaraha urut jajahan, s Namibia nu populasi pamaké basa Jérmanna kawilang loba, sababaraha minoritas di nagara-nagara Éropa wétan, kawas Rusia, Hungaria, jeung Slovenia, sarta di Amérika Kalér jeung nagara-nagara Amérika Latin kawas Argentina jeung Brazil, utamana di wilayah Rio Grande Do Sul, Santa Catarina, Paraná e Espírito Santo
Urang Amish jeung sabagian Mennonites ogé maké logat Jérman
Ampir 120 juta jalma, saparapat ti sakabéh Éropa, ngomong basa Jérman
Basa Jérman jadi basa katilu pangpopularna nu diajarkeun di sakuliah dunya, sarta kadua pangpopularna di Éropa (sanggeus basa Inggris), AS, jeung Asia Wétan (Jepang)
Basa Jérman jadi basa resmi di Uni Éropa
Logat nu mangrupa géséhan sora basa Jérman kadua nalika mangsa pertengahan dianggep salaku bagian ti basa Jérman modérn
Ayana pola jajahan, Völkerwanderung (diéjah ['fœlk6vand@rUN]), jalur dagang jeung komunikasi (wewengkon aliran walungan), jeung isolasi lingkungan (pagunungan jeung leuweung geledegan) ngabalukarkeun tumuwuhna lentong/basa wewengkon nu baréda
Basa-basa wewengkon ieu dipaké sapanjang Karajaan Romawi
Nalika Jérman papisah kana sababaraha nagari, kakuatan hiji-hijina nu nyaragemkeun or standarisasi basa Jerman nyaéta a long process of several hundred yéars, in which writers tried to write and in a way, that was understood in the largest aréa
When Martin Luther translated the Bible (the New Testament in 1521 and the Old Testament in 1534) he based his translation mainly on this alréady developed language, which was the most widely understood language at this time
In the beginning, copies of the Bible had a long list for éach region, which translated words unknown in the region into the regional dialect
Roman Catholics rejected Luther's translation in the beginning and tried to créate their own Catholic standard ("Gemaines Deutsch")
It took until the middle of the 18th century to créate a standard, that was widely accepted, thus ending the period of Early New High German
German used to be the language of commerce and government in the Habsburg Empire, which encompassed a large aréa of Central and éastern Europe
Until the mid-nineteenth century it was essentially the language of townspéople throughout most of the Empire
It indicated that the spéaker was a merchant, an urbanite, not their nationality
Some towns, such as Prague and Budapest were gradually Germanized in the yéars after their incorporation into the Habsburg domain
Others, such as Bratislava ("Pressburg"), were originally settled during the Habsburg period and were primarily German at that time
A few towns such as Milano remained primarily non-German
However, most towns such as Prague, Budapest, Bratislava, Zagreb, and Ljubljana which later became national capitals were for the time primarily German, although they were surrounded by country that spoke other languages
Until about 1800, Standard German was almost only a written language
In this time péople in urban, northern Germany, who spoke dialects very different from Standard German léarnt it almost like a foreign language and tried to pronounce it as close to the spelling as possible
Later this spoken form spréad southward
Media and written works are almost all produced in this variety of High German (usually called Standard German in English or "Hochdeutsch" in German), which is understood in all aréas of German languages (except by pre-school children in aréas which spéak only dialect - but in the age of TV even they usually léarn to understand Standard German before school age)
The first dictionary of the Brothers Grimm, the 16 parts of which were issued between 1852 and 1960, remains the most comprehensive guide to the words of the German language
In 1860, grammatical and orthographical rules first appéared in the "Duden Handbook"
In 1901, this was declared the standard definition of the German language in these matters
Official revisions of some of these rules were not issued until 1998
Basa Jérman mangrupa anggota cabang Kulon kulawarga basa Jérman, nu mangrupa bagian ti kulawarga basa Indo-Éropa
Basa Jérman mangrupa hiji-hijina basa resmi di Jérman, Liechtenstein, jeung Austria; salah sahiji basa resmi di Bélgia (jeung basa Prancis jeung basa Walanda), Itali (jeung basa Itali, basa Prancis, basa Slovénia), Swis (jeung basa Prancis, Itali, jeung Romansh), Luxembourg (jeung basa Prancis &amp; basa Luxembourg), jeung Denmark (jeung basa Denmark), ogé salah sahiji ti 20 basa resmi Uni Éropa
Basa Jérman ogé mangrupa basa minoritas di Prancis, Rusia, Kazakhstan, Tajikistan, Polandia, Rumania, Togo, Kamérun, Amérika Serikat, Namibia, Brazil, Paraguay, Hungaria, Républik Céko, Slovakia, Walanda, Slovénia, Ukraina, Kroasia, Moldavia, Australia, Latvia, Éstonia, jeung Lituania
Basa Jérman kungsi jadi basantara ("lingua franca") Éropa tengah, wétan, jeung kalér
Pangaruh basa Inggris nu beuki kuat geus mangaruhan basa Jérman kiwari
Ngan, basa Jérman masih tetep jadi salah sahiji basa deunegun pangpopularna nu diajarkeun di sakuliah dunya, jeung leuwih ti basa Prancis di wewengkon Éropa
Kira 38% pangeusi Éropa nyebutkeun bisa ngomong basa Jérman
Istilah "basa Jérman" is used for several dialects of Germany and surrounding countries and in North America
The dialects of Germany are typically divided into Low German and High German
The Low German dialects, or Low Saxon as they are sometimes known more precisely, are more closely related to Lower Franconian languages like Dutch than to the High German dialects, and from a linguist's perspective are not part of the German language proper
The High German dialects spoken by Germanic communities in the former Soviet union and Ashkenazi Jews have several unique féatures, and are usually considered the separate language Yiddish
There are also distinctive dialects of German which are or were primarily spoken in North America, including Pennsylvania German, Texas German, and Hutterite German
The modérn dialects of German proper are divided into Middle German and Upper German; Standard German is based on Middle German, while Austrian and Swiss German dialects are Upper German
A moderately complete listing of these dialects may be found at High German
"Artikel utama: Tata basa Jérman" Sistim tulisan
Basa Jérman ditulis ngagunakeun aksara Latin
Lian ti 26 aksara baku, dina basa Jérman aya tilu vokal nu maké Umlaut, nyaéta "ä", "ö", jeung "ü", ogé lambang husus pikeun "ss", nu ngan dipaké dina kasus husus: ß. Nepi ka abad 20, basa Jérman dicitak dina aksara hideung "Gothic" (Fraktur, atawa Schwabacher) jeung ditulis dina Sütterlin
Varian aksara Latin ieu béda pisan jeung "serif" atawa "sans serif" typefaces nu dipaké kiwari, and are difficult for the untrained to réad
"Artikel utama: Éjahan basa Jérman"
Sénsus nyaéta proses keur néangan informasi ngeunaan unggal anggota dina populasi (henteu ngan keur populasi manusa)
Sénsus ieu mangrupa métode pikeun ngumpulkeun data statistik
Kasimpulan statistik ("") diperlukeun mun susunan observasi hayang disimpulkeun sarta hayang ngajelaskeun sagampang-gampangna
Statistikawan umumna nyoba ngajelaskeun observasi dina Median, mode, sarta interquartile mean mangrupa ukuran lokasi oge
Keur ngajelaskeun dispersi statistik, urang bisa maké rentang, interquartile range, atawa simpangan mutlak
Koefisien Gini asalna diwangun keur ngukur kateusaruaan panghasilan, tapi bisa ogé dipaké keur kaperluan séjénna
balik ka tiori statistik -- summarizing statistical data
Statistik déskriptif nyaéta hiji cabang statistik nu ngagambarkeun ayana sababaraha téhnik nu dipaké keur nyokot kasimpulan tina runtuyan data
Dina hal ieu nyaéta ngagunakeun data anggota keur ngajelaskeun runtuyan data
Téhnikna biasana digolongkeun kana: Sacara umum, data statistis bisa didadarkeun salaku daptar "subjék" atawa "unit" katut data nu patali jeung masing-masingna
Sanajan lolobana panalungtik ngagunakeun sababaraha tipe data keur unggal satuan, urang watesan ngan keur hiji bagéan data keur méré gambaran mimiti
Aya dua pasualan nu hayang dijéntrékeun nyaéta: Lamun urang nyimpulkeun lobana hal saperti panjang atawa beurat atawa umur, geus ilahar ngagunakeun rata-rata aritmetika, median, atawa mode
Kadangkala, milih nilai husus tina fungsi sebaran kumulatif nu disebut kuantil
Ukuran variabiliti ilahar keur data kuantitatip nyaéta varian; akar kuadratna nyaéta simpangan baku; rentang; interquartile range; sarta simpangan mutlak
Statistik rékayasa atawa statistika téhnik ("engineering statistics") nyaéta cabang statistika nu ngawengku dua subtopik nu husus rékayasa: Rujukan
Téorema Bayes mangrupa hasil dina tiori probabiliti, which gives the conditional probability distribution of a variabel acak "A" given "B" in terms of the conditional probability distribution of variable "B" given "A" and the marginal probability distribution of "A" alone
In the context of Bayesian probability theory and statistical inference, the marginal probability distribution of "A" alone is usually called the "prior probability distribution" or simply the "prior"
The conditional distribution of "A" given the "data" "B" is called the "posterior probability distribution" or just the "posterior"
As a mathematical theorem, Bayes' théorem is valid regardless of whether one adopts a frequentist or a Bayesian interpretation of probability
However, there is disagreement as to what kinds of variables can be substituted for "A" and "B" in the théorem; this topic is tréated at gréater length in the articles on Bayesian probability and frequentist probability
Bayes' théorem is named after the Reverend Thomas Bayes (1702–61)
Bayes worked on the problem of computing a distribution for the paraméter of a binomial distribution (to use modérn terminology); his work was edited and presented posthumously (1763) by his friend Richard Price, in "An Essay towards solving a Problem in the Doctrine of Chances"
Bayes' results were replicated and extended by Laplace in an essay of 1774, who apparently was not aware of Bayes' work
One of Bayes' results (Proposition 5) gives a simple description of conditional probability, and shows that it does not depend on the order in which things occur: "If there be two subsequent events, the probability of the second b/N and the probability of both together P/N, and it being first discovered that the second event has also happened, the probability I am right "[i.e
the conditional probability of the first event being true given that the second has happened]" is P/b."
The main result (Proposition 9 in the essay) derived by Bayes is the following: assuming a uniform distribution for the prior distribution of the binomial paraméter "p", the probability that "p" is between two values "a" and "b" is formula_1 where "m" is the number of observed successes and "n" the number of observed failures
His preliminary results, in particular Propositions 3, 4, and 5, imply the result now called Bayes' Théorem (as described below), but it does not appéar that Bayes himself emphasized or focused on that result
What is "Bayesian" about Proposition 9 is that Bayes presented it as a probability for the paraméter "p"
That is, not only can one compute probabilities for experimental outcomes, but also for the paraméter which governs them, and the same algebra is used to maké inferences of either kind
Interestingly, Bayes actually states his question in a way that might maké the idéa of assigning a probability distribution to a paraméter palatable to a frequentist
He supposes that a billiard ball is thrown at random onto a billiard table, and that the probabilities "p" and "q" are the probabilities that subsequent billiard balls will fall above or below the first ball
By making the binomial paraméter "p" depend on a random event, he cleverly escapes a philosophical quagmire that he most likely was not even aware was an issue
Statement of Bayes' theorem
Bayes' théorem is a relation among conditional and marginal probabilities
It can be viewed as a méans of incorporating information, from an observation, for example, to produce a modified or updated probability distribution
Suppose the marginal probability density function or probability mass function of a random variable "X" is formula_2 (be very careful to distinguish between the capital "X" and the lower-case "x" above!)
This is the "prior probability distribution" of "X"
Suppose the conditional probability density function or probability mass function of "Y" given "X" = "x" (a function of "y") is formula_3 As a function of "x", this is the likelihood function formula_4 The likelihood function is not a probability density function or a probability mass function for "X", since it need not integrate (or sum) over "x" to produce 1
Bayes théorem says: To get the posterior probability distribution of "X" (i.e., the conditional probability distribution of "X" given "Y"), multiply the prior probability density function (or mass function) for "X" by the likelihood function, and then normalize to produce a probability distribution
"Normalize" méans to multiply or divide by a constant to maké the resulting function a probability density function or a probability mass function
Thus the posterior probability density function is formula_5 The normalizing constant in the denominator is formula_6 In the discrete case, one would have a sum rather than an integral
If one takes the measure-theoretic viewpoint, either is an integral
Suppose the proportion "R" of voters who will vote "yes" in a referendum is sebaran seragam between 0 and 1
That is the prior probability distribution of "R"
A random sample of 10 voters is taken, and it is found that seven of them will vote "yes"
The conditional distribution of the number "X" of voters in this small sample who will vote "yes", given that (capital) "R" is some particular number (lower-case) "r", is a sebaran binomial with paraméters 10 and "r", i.e., it is the distribution of the number of "successes" in 10 independent Bernoulli trials with probability "r" of success on éach trial
One therefore has formula_7 Since "X" was observed to be 7, the likelihood function is formula_8 for 0 ≤ "r" ≤ 1
The prior probability density function is formula_9 and 0 otherwise
Multiplying the prior by the likelihood, we get formula_10 if 0 ≤ "r" ≤ 1, and 0 otherwise
Integrating, we get formula_11 so the posterior probability density function is formula_12 for "r" between 0 and 1, and 0 otherwise
One may be interested in the probability that more than half the voters will vote "yes"
The "prior probability" that more than half the voters will vote "yes" is 1/2, by the symmetry of the sebaran seragam
The posterior probability that more than half the voters will vote "yes", i.e., the conditional probability given the outcome of the opinion poll—that seven of the 10 voters questioned will vote "yes"—is formula_13 about an "89% chance"
Derivation in the discrete case
To derive Bayes' théorem in the discrete case, note first from the definition of conditional probability that formula_14 denoting by "P"("A","B") the joint probability of "A" and "B"
Dividing the left- and right-hand sides by "P"("B"), we obtain formula_15 which is Bayes' théorem
éach term in Bayes' théorem has a conventional name
The term "P"("A") is called the "prior probability" of "A"
It is "prior" in the sense that it precedes any information about "B"
"P"("A") is also the "marginal probability" of "A"
The term "P"("A"|"B") is called the "posterior probability" of "A", given "B"
It is "posterior" in the sense that it is derived from or entailed by the specified value of "B"
The term "P"("B"|"A"), for a specific value of "B", is called the "likelihood function" for "A" given "B" and can also be written as "L"("A"|"B")
The term "P"("B") is the prior or marginal probability of "B", and acts as the "normalizing constant"
Alternative forms of Bayes' theorem
Bayes' théorem is often embellished by noting that formula_16 so the théorem can be restated as formula_17 where "A""C" is the complementary event of "A"
More generally, where {"A""i"} forms a partition of the event space, formula_18 for any "A""i" in the partition
See also the law of total probability
Bayes' theorem for probability densities
There is also a version of Bayes' théorem for continuous distributions
It is somewhat harder to derive, since probability densities, strictly spéaking, are not probabilities, so Bayes' théorem has to be established by a limit process; see Papoulis (citation below), Section 7.3 for an elementary derivation
Bayes' théorem for probability densities is formally similar to the théorem for probabilities: formula_19 and there is an analogous statement of the law of total probability: formula_20 As in the discrete case, the terms have standard names
"f"("x", "y") is the joint distribution of "X" and "Y", "f"("x"|"y") is the posterior distribution of "X" given "Y"="y", "f"("y"|"x") = "L"("x"|"y") is (as a function of "x") the likelihood function of "X" given "Y"="y", and "f"("x") and "f"("y") are the marginal distributions of "X" and "Y" respectively, with "f"("x") being the prior distribution of "X"
Here we have indulged in a conventional abuse of notation, using "f" for éach one of these terms, although éach one is réally a different function; the functions are distinguished by the names of their arguments
Extensions of Bayes' theorem
Théorems analogous to Bayes' théorem hold in problems with more than two variables
These théorems are not given distinct names, as they may be mass-produced by applying the laws of probability
The general strategy is to work with a decomposition of the joint probability, and to marginalize (integrate) over the variables that are not of interest
Depending on the form of the decomposition, it may be possible to prove that some integrals must be 1, and thus they fall out of the decomposition; exploiting this property can reduce the computations very substantially
A Bayesian network is essentially a mechanism for automatically generating the extensions of Bayes' théorem that are appropriate for a given decomposition of the joint probability
Typical examples that use Bayes' théorem assume the philosphy underlying Bayesian probability that uncertainty and degrees of belief can be méasured as probabilities
For worked out examples, pléase see the article on the examples of Bayesian inference
Dina matematika, sebaran probabilitas nangtukeun unggal interval tina wilangan nyata kamungkinan, mangka kitu aksioma probabilitas "terpenuhi"
Dina watesan téhnik, probabiliti sebaran nyaéta ukuran probabilitas nu mana domain mangrupa aljabar Borel dina kaayaan riil
Probabilitas sebaran dina kasus husus mangrupa notasi nu leuwih tina ukuran probabilitas, nyaéta fungsi nu assigns probabilities satisfying the Kolmogorov axioms to the méasurable sets of a measurable space
Unggal variabel acak gives rise to a probability distribution, and this distribution contains most of the important information about the variable
If "X" is a random variable, the corresponding probability distribution assigns to the interval ["a", "b"] the probability Pr["a" ≤ "X" ≤ "b"], i.e
the probability that the variable "X" will take a value in the interval ["a", "b"]
Sebaran probabilitas variabel "X" bisa sacara unik didadarkeun ku fungsi sebaran kumulatif "F"("x"), nu ditangtukeun ku formula_1 pikeun "x" anggota R. A distribution is called "discrete" if its cumulative distribution function consists of a sequence of finite jumps, which méans that it belongs to a discrete random variable "X": a variable which can only attain values from a certain finite or countable set
A distribution is called "continuous" if its cumulative distribution function is continuous, which méans that it belongs to a random variable "X" for which Pr[ "X" = "x" ] = 0 for all "x" in R. The so-called "absolutely continuous distributions" can be expressed by a fungsi dénsitas probabilitas: a non-negative Lebesgue integrable function "f" defined on the réals such that formula_2 for all
réals such that formula_2 for all "a" and "b"
That discrete distributions do not admit such a density is unsurprising, but there are continuous distributions like the devil's staircase that also do not admit a density
The "support" of a distribution is the smallest closed set whose complement has probability zero
Jejer penting dina sebaran probabiliti
Sababaraha sebaran probabiliti kacida pentingna dina téori atawa pamakéan dibéré ngaran nu husus: Tempo ogé
daptar jejer statistis -- variabel acak -- cumulative distribution function -- probability density function -- likelihood
Mangga tambahkeun artikel Wikipedia ngeunaan statistik anu euweuh dina jejer di handap ieu
Parobahan anu aya hubunganna jeung kaca di handap ieu bakal ditujukeun kana kaca panganyarna tina éta jejer
Keur nempo parobahan anyar dina "kaca" ieu, tempo ogé kaca sajarah
Tempo ogé list of probability topics, jeung daptar statistikawan
Kriging nyaéta téhnik regression anu digunakeun dina géostatistik
Ngaran ieu digunakeun sasuai jeung nu manggihkeunna nyaéta, Danie G. Krige
Dina komunitas statistik, leuwih umum disebut Gaussian process regression
Kriging bisa dipikaharti leuwih jéntré dina bentuk inferensi Bayes
Kriging dimimitian ku prior distribution dina fungsi
Mimiti ieu meunang ngitung dina Gaussian process: formula_1 sampel ti hiji fungsi bakal kasebar sacara normal, di mana kovarian antara dua sampel téh mangrupa fungsi covariance (atawa kernel) ti prosés Gauss dina dua titik lokasi spasial
Saterusna susunan data ditalungtik, unggal nilai digabungkeun ku lokasi spasial
Ayeuna, nilai anyar bisa "diprediksi" di unggal lokasi spasial anyar, ku kombinasi Gauss awal jeung Gaussian likelihood unggal nilai observasi
Hasilna posterior distribusi ogé Gaussian, nu mana méan jeung covariance leuwih sederhana diitung tina nilai observasi, variance-na, jeung matrix kernel asalna téh ti awal (prior)
Dumasar kana "sudut pandang" géologi, Kriging ngagunakeun pangaweruh awal ngeunaan sebaran spasial mineral: pangaweruh awal ieu téh nyaéta kumaha dongengna mineral bisa mangrupa fungsi ruang
Saterusna, ditambahkeun angka-angka konsentrasi mineral, Kriging bisa "memprediksi" konsentrasi mineral dina titik anu teu katalungtik
Géostatistik maké téori proses stokastik jeung kasimpulan statistik keur nalungtik fénoménagéografi
Géostatistik geus ilahar digunakeun dina widang géo-sciences
métodeu Géostatistik ogé dipaké di geologi minyak, hidrogeologi, meteorologi, oseanografi, geokimia, kahutanan, kontrol lingkungan, ekologi lanskap, pertanian (hususna keur kacocogan tatanen) jeung sajabana
Konsép dasar géostatistik nyaéta yén skala mangrupa "variasi spasial"
Data spasial "terikat" nunjukkeun yén variabiliti hampir sarua "tanpa" ningali lokasi titik data
Sanajan kitu, data spatial dina sababaraha kasus lain mangrupa data "terikat"
Nilai data nu mana raket sacara spatial mibanda "variabiliti" anu saeutik dibandingkeun jeung nilai nu mana leuwih jauh antara hiji kanu séjénna
Sifat pola ieu béda-béda tina hiji susunan data ka susunan data nu séjénna; unggal susunan data mibanda fungsi nu "unik" sarta jarak antara dua titik data
Variabilitas ieu ilaharna diitung salaku fungsi nu disebut semivarian
Spatial autocorrelation bisa diitung ngagunakeun correlograms, covariance functions jeung variograms (=semivariograms)
Topik anu aya hubunganna: statistik, géologi, GIS, remote sensing, kriging
Géologi (tina basa Yunani γη- ("ge-", "bumi") and λογος ("logos", "kecap", "alesan")) nyaéta élmu jeung pangajaran ngeunaan bumi, kaasup kajadiannana, strukturna, sifat fisikna, sajarah, jeung prosés "pembentukannana", mangrupa salah sahiji bagéan elmu bumi
Ahli geologi netepkeun umur bumi kira-kira 4.6 milyar (4.6x109) taun, sarta nangtukeun yén litosfer bumi, nu kaasup kerak, nyaéta bagéan tina tektonik lempeng nu pipindahan dina saluhureun rheic mantel bagian luhur (asthenosphere) ngaliwatan prosés nu nujul kana lempeng-lempeng tektonik
Ahli géologi mantuan néangan tur ngokolakeun sumber daya alam Bumi, saperti minyak bumi jeung batubara, ogé logam saperti beusi, tambaga, jeung uranium