Questions tagged [baum-welch]

The Baum–Welch algorithm is used to find the unknown parameters of a hidden Markov model (HMM).

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Hidden Markov Model - Baum Welch algorithm initialization

Currently I'm working on a problem where I have a multidimensional, continuous sequence of observations $X$ that model my response variable $y$ with two states $0$ and $1$. I assume that this sequence ...
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Baum-Welch Algorithm and Viterbi Algorithm

Given a sequence of actual states which is assumed to be the sequence that has the maximum likelihood of causing an observed sequence of states, is it possible to find the parameters such that when ...
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Training a layered HMM

I am currently planning on training a layered Hidden Markov Model. I have 3 stages with the following structures. The first stage is a 3-state HMM with the State X: can emit insertion errors State Y: ...
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How do I check my own Baum Welch Algorithm

Given an observation {1,1,1,1,1,1,1,1,0} Transition matrix$\,$A \begin{bmatrix} 0.5 & 0.5 \\ 0.3 & 0.7 \end{bmatrix} Emission matrix$\,$B \begin{bmatrix} 0.3 & 0.7 \\ 0.8 &...
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Optimizing HMM log-likelihood with time-dependent prior

I have a HMM (Hidden Markov Model) which emits an observation Z. The parameters of the HMM are $\boldsymbol\theta$. $$\boldsymbol\theta = {\boldsymbol{A},\boldsymbol{B},\pi}$$ Where $\boldsymbol{A}$ ...
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Initialisation strategies for learning Hidden Markov Models

I used hmmlearn library to initialize an HMM (Hidden Markov Model). sampled observations from the HMM, and used the sampled data to re-estimate the parameters of ...
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Bayes Theorem for Baum-Welch algorithm

I've been reading the Baum-Welch algorithm and somewhere it mentioned this statistical property: $$ P(X \mid Y,Z) =\frac{ P(X,Y \mid Z)}{P(Y \mid Z)} $$ being based on Bayes' Theorem. I do understand ...
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Expectation Maximisation vs Expectation Propagation in the context of Bayesian Networks

I am confused about Expectation Maximisation and Expectation Propagation algorithms in the context of Bayesian Networks, especially whether one comprise another. What is the difference between ...
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Example of manual implementation of baum-welch algorithm in R

Is there any code out there that implements the baum-welch algorithm for a very basic problem? It would be very helpful to actually see the algorithm in action to better understand how it works. I ...
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Minimum Population Size for T-Test

I'm comparing two different populations with unequal variance and non normal distributions. For sample #1 I'm drawing a random sample size of $n=30$ from a population of 200. For sample #2 I'm ...
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Deciding length of units in sound recognition for training HMMs

I am working on creating a method to detect changes from one song to another. Namely, I hope to use a Hidden Markov Model (HMM) in order to model a part of a song and check to see if it accurately ...
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Viterbi and forward-backward algorithm in HMM

I am learning HMM recently and got confused with the training problem (training model parameters and hidden state given outcome sequence). As far as I know, both Viterbi learning and Baum-Welch (...
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How do I report a Welch's test?

I ran a Welch's test with Alpha= 0.05 followed by a Games Howell test. The Welch's test is not significant (p-value=0.104) Is it enough to just mention that the groups were not significantly different ...
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What are some applications of unsupervised HMMs?

Supervised HMMs can be applied to many problems like POS tagging and OCR (optical character recognition). I've learned that HMMs can be trained unsupervisedly using EM (Baum-Welch algorithm), what ...
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MFCCs and MoG-HMMs for speech recognition

BACKGROUND MFCCs are coefficients which represent the most important parts of speech, and about 12 of them are used to model a one 512 points long frame (of speech). Along with them you would use ...
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Simple Explanation of Baum Welch/Viterbi

I'm looking for a very simple explanation as possible for Baum Welch and Viterbi for HMMs with a straightforward well annotated example. Almost all of the explanations I find on the net invariably ...
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What if transitions and emissions in a hidden Markov model are not independent?

A Hidden Markov Model is given by transition and emission matrices. The transition matrix determine probability of "next states" as a function of the current state. The emission matrix determine the ...
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Baum-Welch and hidden Markov models: Continuous observation densities in HMMs

I am currently trying to understand how parameter are being reestimated for hidden Markov models (HMMs), using expectation-maximization (EM). What I seem to have problems understanding is what the ...
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Produce Multiple Sequence Alignments using Profile HMMs

I have a few multiple sequence alignments and a few groups (let's say N) of unaligned sequences. I would like to learn the emission and transition probabilities from the multiple sequence alignments, ...
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Baum Welch training of HMM

I have 200k sequences and each element of the sequence is vector of length 200. I plan to learn a HMM using this data, using the Baum-Welch EM algorithm to infer transition and emission probabilities. ...
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Forward-backward algorithm for HMM

I am currently studying this paper In which i am having some problems understanding the purpose of the forward-backwards algorithm. First of all why even have both forward and backwards? It seems ...
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re-estimation of emission probabilities in HMM

I am confused about the re-erstimation procedure for emissions in HMMs with Baum-Welch (still). I posted two questions concerning this general topic already and I thought I had cleared up my confusion,...
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Do re-estimated HMM parameters still need to be normalized?

A few days ago I asked this question. I only got one answer and I did not really understand it. Now I think this question is a special case of a more general question I have, namely: Do re-estiamted ...
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Scaling step in Baum-Welch algorithm

I am implementing the Baum-Welch Algorithm for training a Hidden Markov Process, to basically better understand the training process. I have implemented the iterative procedures described in Rabiner'...
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Can the distribution of emission probabilities of an HMM be swapped out for the re-estimated ones only after all training sequences have been covered?

Regarding the re-estimation procedure of the Baum-Welch algorithm, the sources I looked into so far all describe the process in an abstract manner. Therefore I am wondering the following about ...
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combining log likelihoods from different series of observations

I have generated multiple log likelihood estimates for some parameters $\theta$ based on independent sets of observation. How can I combine these into one average?
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Hidden Markov Model EM Training: Linking states to hidden variables

I want to implement the Baum Welch Algorithm for unsupervised training of Hidden Markov Models, I am however still a little unclear on some details, especially this one: Before learning the optimal ...
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EM algorithm update step formula

I'm using these formulas to update my initial vector, transition matrix and emission matrix: I have update my initial vector fine using formula (1) but the other two formula have an alpha instead of ...
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Calculating alpha in EM / Baum-Welch algorithm for Hidden Markov

I am trying to use this equation to calculate the alpha (forward) probabilities for the EM/Baum-welch algorithm but I'm running into some confusion. I don't understand what the $h_t$ is. I know its a ...
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Which parameters need to be initialized random for gaussian mixture hidden markov model?

So, if I model observation probability for a given hidden state according to a multivariate gaussian mixture model, then which parameters need to be initialized random to perform parameter re-...
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baum-welch parameter estimation numeric example

I have an HMM (picture below), with a single parameter $\theta$ I want to estimate using Baum-Welch. I have a single training example X="HHT", and I start with an ...
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HMM (Baum-Welch) - convergence rate differences between the transition and output matrices

I am trying to learn more about the convergence properties of the Baum-Welch algorithm for estimating the HMM parameters. I ran a test comparing the convergence of both the transition and output ...
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HMM with some known inputs

I am trying to solve a problem using HMM where the observations Yt are the result of both known and hidden parameters, but unfortunately, I am not sure how to combine the known and hidden parameters ...
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How to Train HMM model with two different sequences using the Baum-Wech algorithm

I am using HMM to visualize drinking gestures of different container types. I began training HMM with one sequence corresponding to one container type, but I want to visualize it with python now with ...
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Baum-Welch algorithm variation for Hidden Markov model with reward

Following my previous question on the subject I would like to get your feedback on the following alternative solution. (The original solution to this question is the usage of the POMDP model proposed ...
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Baum Welch:Calculating transition probability

I am trying to understand the Baum Welch algorithm by implementing it in xls. I have chosen a simple example of observations from a loaded (L) vs fair (F) die. I calculate the forward and backward ...
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In what way does clustering help in classification?

I understand that with a K-means or DTW algorithm one can cluster time series using a distance criterion, i also understand that with a K-NN algorithm for example one can do pattern recognition and ...
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How do I find the hidden states of a HMM for a non time series data? How does EM Algorithm play a role in HMM?

I am a beginner in machine learning. My project requires me to model user intentions using HMM i.e based on parameters like no.of clicks, time on page, etc I have to model whether the user wants to ...
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2 votes
2 answers
2k views

How to model Hidden markov model with changing transition probability

I have a series of observations that fall into two outcomes, 0 or 1. These observations have an associated time of observation, as well as additional features that I can gather for that observation. I ...
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21 votes
2 answers
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What are the differences between the Baum-Welch algorithm and Viterbi training?

I am currently using Viterbi training for an image segmentation problem. I wanted to know what the advantages/disadvantages are of using the Baum-Welch algorithm instead of Viterbi training.
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