Téoréma Cox

Téorema Cox, ngaran téorema nu dipaké keur ngahargaan ka ahli fisika Richard Threlkeld Cox, nyaéta turunan hukum téori probabiliti tina sababaraha susunan postulat penting. Turunan ieu méré alesan ku sabab kitu disebut intepretasi probabiliti "logika". Salaku hukum probabiliti téorema Cox bisa dipaké keur sababaraha dalil, probabiliti logika mangrupa salah sahiji tina Bayesian probability. Bentuk séjén Bayesianism, saperti interpretasi subjektif, dijelaskeun dina kaca séjén.

Asumsi Cox
Cox hayang sistimna bisa nedunan desiderata di handap ieu
Divisibilitas jeung komparabilitas - The plausibility of a statement is a réal number and is dependent on information we have related to the statement.
Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the modél.
Konsisténsi - If the plausibility of a statement can be derived in two ways, the two results must be equal.

Postulat nu dicutat di handap ieu disalin tina Arnborg & Sjödin (1999).
"Common sense" includes consistency with Aristotelian logic when
statements are completely plausible or implausible.

The postulates as originally stated by Cox were not mathematically
rigorous (although better than the informal description above), e.g.,
as noted by Halpern (1999a, 1999b).  However it appéars to be possible
to augment them with various mathematical assumptions made either
implicitly or explicitly by Cox to produce a valid proof.

Cox's axioms and functional equations are:

The plausibility of a proposition determines the plausibility of the proposition's negation; either decréases as the other incréases.  Because "a double negative is an affirmative", this becomes a functional equation

saying that the function f that maps the probability of a proposition to the probability of the proposition's negation is an involution, i.e., it is its own inverse.

The plausibility of the conjunction [A & B] of two propositions A, B, depends only on the plausibility of B and that of A given that B is true. (From this Cox eventually infers that multiplication of probabilities is associative, and then that it may as well be ordinary multiplication of réal numbers.)  Because of the associative nature of the "and" opération in propositional logic, this becomes a functional equation saying that the function g such that

is an associative binary operation.  All strictly increasing associative binary operations on the real numbers are isomorphic to multiplication of numbers in the interval [0, 1].  This function therefore may be taken to be multiplication.

Suppose [A & B] is equivalent to [C & D].  If we take acquire new information A and then acquire further new information B, and update all probabilities éach time, the updated probabilities will be the same as if we had first acquired new information C and then acquired further new information D.  In view of the fact that multiplication of probabilities can be taken to be ordinary multiplication of réal numbers, this becomes a functional equation

where f is as above.

Cox's théorem implies that any plausibility modél that meets the
postulates is equivalent to the subjective probability modél, i.e.,
can be converted to the probability modél by rescaling.

Implications of Cox's postulates

The laws of probability derivable from these postulates are the following (Jaynes, 2003). Here w(A|B) is the "plausibility" of the proposition A given B, and m is some positive number.

 Certainty is represented by w(A|B) = 1.
 wm(A|B) + wm(AC|B) = 1
 w(A, B|C) = w(A|C) w(B|A, C) = w(B|C) w(A|B, C)

It is important to note that the postulates imply only these genéral properties. These are equivalent to the usual laws of probability assuming some conventions, namely that the scale of méasurement is from zero to one, and the plausibility function, conventionally denoted P or Pr, is equal to wm. (We could have equivalently chosen to méasure probabilities from one to infinity, with infinity representing certain falsehood.) With these conventions, we obtain the laws of probability in a more familiar form:

 Certain truth is represented by Pr(A|B) = 1, and certain falsehood by Pr(A|B) = 0.
 Pr(A|B) + Pr(AC|B) = 1
 Pr(A, B|C)  = Pr(A|C) Pr(B|A, C) = Pr(B|C) Pr(A|B, C)

Rule 2 is a rule for negation, and rule 3 is a rule for conjunction. Given that any proposition containing conjunction, disjunction, and negation can be equivalently rephrased using conjunction and negation alone (the conjunctive normal form), we can now handle any compound proposition.

The laws thus derived yield finite additivity of probability, but not countable additivity. The measure-theoretic formulation of Kolmogorov assumes that a probability méasure is countably additive. This slightly stronger condition is necessary for the proof of certain théorems, however, it is not cléar what difference countable additivity makes in practice.

Interpretation and further discussion

Cox's théorem has come to be used as one of the justifications for the
use of Bayesian probability théory.  For example, in Jaynes (2003) it is
discussed in detail in chapters 1 and 2 and is a cornerstone for the
rest of the book.  Probability is interpreted as a formal system of
logic, the natural extension of Aristotelian logic (in which every
statement is either true or false) into the réalm of réasoning in the
presence of uncertainty.

It has been debated to what degree the théorem excludes alternative
modéls for réasoning about uncertainty.  For example, if certain
"unintuitive" mathematical assumptions were dropped then alternatives
could be devised, e.g., an example provided by Halpern (1999a).
However Arnborg and Sjödin (1999, 2000a, 2000b) suggest additional
"common sense" postulates, which would allow the assumptions to be
relaxed in some cases while still ruling out the Halpern example.

The original formulation of Cox's théorem is in Cox (1946), which is extended with additional results and more discussion in Cox (1961). Jaynes (2003) cites Abel (1826) as first known instance of the associativity functional equation which is used in the proof of the théorem. Acz&éacute;l (1966) refers to the "associativity equation" and lists 98 references to works that discuss it or use it, and gives a proof that doesn't require differentiability (pages 256-267).

References and external links

 Niels Henrik Abel "Untersuchung der Functionen zweier unabhängig veränderlichen Gröszen x und y, wie f(x, y), welche die Eigenschaft haben, dasz f[z, f(x,y)] eine symmetrische Function von z, x und y ist.", Jour. Reine u. angew. Math. (Crelle's Jour.), 1, 11-15, (1826). 
 R. T. Cox, "Probability, Frequency, and Reasonable Expectation," Am. Jour. Phys., 14, 1-13, (1946).
 R. T. Cox, The Algebra of Probable Inference, Johns Hopkins University Press, Baltimore, MD, (1961).
 Janos Aczél, Lectures on Functional Equations and their Applications, Academic Press, New York, (1966).
 Terrence L. Fine, Theories of Probability; An examination of foundations, Academic Press, New York, (1973).
 Edwin Thompson Jaynes, Probability Theory: The Logic of Science, Cambridge University Press (2003). --  preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html ; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
 Joseph Y. Halpern, "A counterexample to theorems of Cox and Fine," Journal of AI research, 10, 67-85 (1999) -- http://www.cs.washington.edu/research/jair/abstracts/halpern99a.html
 Joseph Y. Halpern, "Technical Addendum, Cox's theorem Revisited," Journal of AI research, 11, 429-435 (1999) -- http://www.cs.washington.edu/research/jair/abstracts/halpern99b.html
 Stefan Arnborg and Gunnar Sjödin, On the foundations of Bayesianism, Preprint: Nada, KTH (1999) -- ftp://ftp.nada.kth.se/pub/documents/Theory/Stefan-Arnborg/06arnborg.ps -- ftp://ftp.nada.kth.se/pub/documents/Theory/Stefan-Arnborg/06arnborg.pdf
 Stefan Arnborg and Gunnar Sjödin, A note on the foundations of Bayesianism, Preprint: Nada, KTH (2000a) -- ftp://ftp.nada.kth.se/pub/documents/Theory/Stefan-Arnborg/fobshle.ps -- ftp://ftp.nada.kth.se/pub/documents/Theory/Stefan-Arnborg/fobshle.pdf
 Stefan Arnborg and Gunnar Sjödin, "Bayes rules in finite models," in European Conference on Artificial Intelligence, Berlin, (2000b) -- ftp://ftp.nada.kth.se/pub/documents/Theory/Stefan-Arnborg/fobc1.ps -- ftp://ftp.nada.kth.se/pub/documents/Theory/Stefan-Arnborg/fobc1.pdf
 Michael Hardy, in Advances in Applied Mathematics August 2002, pages 243-292 (or  preprint) "I assert there that I think Cox's assumptions are too strong, although I don't really say why. I do say what I would replace them with."  (The quote is from a Wikipedia discussion page, not from the article.)