Talk:Hidden Markov model

message: In the second sentence we can read:" In a hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible". It's me or it is a little difficult to understand?
141.244.140.173 (talk) 10:42, 7 March 2014 (UTC)
thread_title: First paragraph

message: Is the reference to Plato's allegory absolutely necessary ? I don't feel like it helps the explanation here.
:Agreed, Plato's cave seems like a bit of a stretch here. Probably a more down-to-earth explanation would be appropriate. Feel free to rework it if you have a better expression. Happy editing, Wile E. Heresiarch 15:31, 31 May 2004 (UTC)
thread_title: Untitled

message: [] is not the whole hidden markov model. The initial probabilities are missing. I think this diagram is confusing, as one cannot continue from the symbols back to the states. I don't really know what this diagram actually shows. It's a markov model of the first step of a hidden markov process, where the observed symbols are illustrated with states. Is this really helpful? It's not a markov model of the whole process and it's not the hidden markov model... Maximilianh (talk) 13:54, 7 July 2010 (UTC)
thread_title:  First Diagram 

message: I've added the genie-ball-urn model from Rabiner 89 just at the start of the article. People who search for hmm in wikipedia are searching for something understandable. As it is now, research papers about hmms like Rabiner's one are much more understandable and accessible than wikipedia. I think the urn-model is much more readable to someone who has never attended a lecture on markov processes than the current article. I also discussed why people actually use HMMs and why the urn-model is just one possible model. There is some overlap with the current article.
Maximilianh (talk) 11:28, 7 July 2010 (UTC)
thread_title:  Mental Model 

message: state 2 doesn't produce the star so sequences 2 and 4 aren't possible for the observed sequence. 
I don't know how to correct this error.    —Preceding unsigned comment added by Leven101 (talk • contribs) 15:42, 1 October 2009 (UTC)
thread_title:  error in image File:HMMsequence.svg (3rd diagram) 

message: The use of HMMs in bioinformatics references Durbin et all from 1999:
In the second half of the 1980s, HMMs began to be applied to the analysis of biological sequences, in particular DNA. Since then, they have become ubiquitous in the field of bioinformatics.[3]
I think a far better reference would be Churchill GA (1989) Stochastic models for heterogeneous DNA sequences. Bull Math Biol 51:79–94, which I think might be the first paper suggesting that a DNA sequence is determined by states in a Markove Model.  —Preceding unsigned comment added by 209.222.206.50 (talk) 16:03, 22 April 2009 (UTC)
thread_title: bioinformatics reference

message: " In this example, there is only a 30% chance that tomorrow will be sunny if today is rainy. "
Rationally, after a rainy day, the likely chance of a sunny day is much higher since rain is a release of water that was deposited in the sky as clouds during sunny days. . .
Shushinla 06:10, 14 October 2005 (UTC)
replies: text: It's only a hypothetical example. --MarkSweep✍ 06:41, 14 October 2005 (UTC)
thread_title:  About rain. 

message: Not every paper that makes use of HMMs belongs in the References section (eg this one doesn't) - what makes these papers suitable? — ciphergoth 08:54, 2 March 2006 (UTC)
thread_title: [[User:Jiali]]'s references

message: Maybe we can add a paragraph describing the three main functions: Evaluate, Decode and Learn. What do you think? JeDi 09:46, 24 July 2006 (UTC)
thread_title:  The three functions of HMM 

message: We does everyone think of the following replacement graphs/diagrams in SVG format? --Thorwald 22:51, 10 June 2007 (UTC)
replies: text: The fonts in the second one are unreadably tiny. linas 02:10, 1 September 2007 (UTC)
thread_title: Graphs

message: I removed this article from the computer vision category.  HMM is not unrelated to CV but
# It is not a methodology developed within CV or specific to CV
# HMM is already listed under the machine learning category which is linked to the CV category.
--KYN 08:39, 28 July 2007 (UTC)
thread_title:  Computer vision category 

message: "There exist a variety of algorithms that, while not providing exact results, do provide reasonably good results, with considerably less demand on storage and compute time. These include the forward-backward algorithm ..." (emphasis mine)
Please correct me if I'm wrong, but doesn't the forward-backward algorithm actually compute the exact probability of an observed sequence?  I'm referring to my Biological Sequence Analysis book (Durbin, Eddy, Krogh, & Mitchison), page 58:
"...the full probability can itself be calculated by ... the forward algorithm."
--Loniousmonk 23:22, 30 September 2007 (UTC)
thread_title:  Probability of an observed sequence 

message: I have added a reference to the following article:
:
That reference seems relevant for the passage. However, I would disagree with the following statement (which is why I added a warning):
:"Learning in such a model is difficult, as dynamic-programming techniques can no longer be used to find an exact solution"
Indeed, to my understanding, a factorial HMM is a specific instance of an HMM, such that an exact solution is possible. With K HMM chains, and J possible states for each variable for a FHMM, we need an HMM with JˆK states - which means that a straightforward forward-backward algorithm would quickly be intractable. According to the above reference, a more efficient exact inference is possible (through a junction tree? See references therein), but still quickly intractable. At last they propose a variational algorithm to approximate the inference (hence the citation in the main article). 
Hope this helps, and if people agree with it, the main article shall be changed accordingly.
Jldurrieu (talk) 13:28, 4 May 2011 (UTC)
thread_title:  Extensions: about factorial HMMs 

message: The concrete example is good; however, the probability of the day being rainy or sunny is stated to be 57% and 43% respectively without giving the basis for the conclusion. These probabilities are dependent solely on the state transition diagram and  are not dependent on the output: walk, shop, clean. Starting probabilities only need to be assumed and must total 1.00. Regardless of the starting point, one will arrive at the correct probability of what type of day it was. The probabilities can be solved directly using Gaussian Elimination, the Grassmann Algorithm or indirectly using the Power Method, Jacobi's Method, or The Gauss-Seidel Method,Successive Over-Relaxation.
Here is an implementation in R of the Power Method of solving the probabilities:
P<-matrix(c(
.7,.3,
.4,.6
),ncol=2,byrow=T)
Identity<-matrix(c(
1,0,
0,1
),ncol=2,byrow=T)
A<-P
n<-matrix(c(.6,.4),ncol=2)
c<-1
cat(paste(c,n,'\n'))
while(TRUE){
c<-c+1
nnew<-n%*%A
cat(paste(c,nnew,'\n'))
if(abs(nnew-n)/abs(n)<0.00000001){break}
if(c>1000){break}
n<-nnew
}
This solves the probabilities of a rain or sunny day by iterating to convergence.
[User:RMLane1|RMLane1]] )  — Preceding unsigned comment added by RMLane1 (talk • contribs) 21:30, 30 November 2012 (UTC)
thread_title:  Concrete Example 

message: Substituted at 17:53, 29 April 2016 (UTC)
thread_title: Assessment comment

message: The article claims that a "typical Bayesian HMM" has shared priors for its emission parameters. I'd really like to see some references for that. While a shared prior is used in cases like Dirichlet Process priors, the case given here, which uses Normal-(Inverse) Gamma as a conjugate for Gaussian emissions typically uses separate emission priors for each state, the reason being that otherwise the emission parameters would have to be statistically independent of the state sequence, which they are not. Joint conjugate emission priors aren't even typical for mixture models, which are a special case of HMM (with fixed uniform transitions), cf. https://www.sciencedirect.com/science/article/pii/S0169716105250162.  — Preceding unsigned comment added by 2001:6B0:2:2801:49DB:7A89:1E3B:E421 (talk) 11:54, 16 April 2017 (UTC)
thread_title:  "Typical" Bayesian HMM? 

message: This article seems in quite good shape, and has been rated B-class and top-importance by WikiProject Computational Biology for just over 6 years now (the other WikiProjects have a Start-class rating, but these may need reassessed). I suggest nominating this article for Good Article review, but as I'm not a significant contributor, I'm opening this discussion as recommended at WP:GAI. Pinging  and  as two contributors with recent wiki activity, what are your thoughts? Comments from anyone else are also welcomed, of course. Thanks! Amkilpatrick (talk) 13:00, 21 June 2017 (UTC)
replies: text: It errs slightly on the technical side, but given the nature of the topic it is difficult not to. Overall, I'd suppose a good article proposal. Klbrain (talk) 10:13, 22 June 2017 (UTC)
replies: text: There are three maintenance tags in the lead that need to be addressed before this can pass GA. Argento Surfer (talk) 18:22, 26 January 2018 (UTC)
thread_title:  Proposed Good Article nomination 

message: This section reads like a random copy-paste from a relevant source without indicating what that source was.  Please define the underlying concepts and introduce the subject carefully. StrokeOfMidnight (talk) 04:22, 15 June 2019 (UTC)
replies: text: I agree. Even the definition provides no context. It is also seriously lacking in inline citations. It needs quite a bit of work if it is to maintain its Good Article status. AIRcorn (talk) 09:52, 17 January 2020 (UTC)
thread_title:  "Mathematical description": more context and/or sources needed 

message: Shouldn't that be a mobile phone this day and age. Since it is in a transcluded example (which I think is a very poor choice for plain text content) someone with better transcluded-fu should edit it and maybe just cut and paste it as plain text in the article. No sensible reason for it to be transcluded, unless it is in a huge number of articles, and I doubt the sanity of having it in numbers of articles. -- Cimon Avaro; on a pogostick. (talk) 22:50, 16 July 2021 (UTC)
thread_title:  Telephone?