Hidden Markov Models are used for modelling systems that are assumed to be Markov processes with hidden (i.e. unobserved) states.

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Hidden Markov Models with multiple emissions per state

I want to use Hidden Markov Models for an unsupervised sequence tagging problem. Due to the peculiarities of my application domain (recognition of dialogue acts in conversations), I would like to use ...
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Estimation of TRANS_GUESS and EMIS_GUESS matrices in Baum-Welch algorithm

I would like to ask how do we estimate the probabilities in TRANS_GUESS and EMIS_GUESS matrices (see here http://www.mathworks.com/help/stats/hidden-markov-models-hmm.html#f8288 ) as an input for the ...
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Hidden Markov Models relationship

I have a question regarding a small investigation that I have been conducting into the relationship between the length of observation sequence, T, on which two decoders (BCJR and classic Viterbi) ...
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Hidden Markov Model with conditional observations

I am looking for a research paper that basically describes a hidden markov model that has multiple observations, and some observations that have conditional dependencies. For example, please consider ...
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Probabilities in a Markov Model

I am reading a paper on Markov Models and I am trying to figure out how to compute the probabilities for the $\alpha$-pass. I am given an $N\times N$ matrix $A$, that has the probabilities of ...
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289 views

Predicting the Weather

Given a tree trunk with concentric circles, can we predict the weather for each year? Each concentric circle accounts for a year that the tree has been on the Earth. The innermost circle is the oldest ...
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HMM-forward backward algorithm

Let $x_t, \, t=1, \dots ,T$ be a time series and suppose that $x_t | \xi_t \sim N(\mu_{\xi_t},\sigma^2_{\xi_t})$, where $\xi_t \in [1, \dots,K]$ is a group indicator (or regime or state), the ...
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61 views

Predict observation using Hidden Markov Models

I have a sequence of observations e.g. ["Click","Scroll","Hover","Zoom","Select"]. I need to predict the next value of this observation sequence but not the next hidden state. I know that there are ...
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Stack of HMMs. Does this have a name?

Consider a sequence of tokens. One could fit a Markov model to this sequence by computing the empirical stochastic transition matrix. A refinement to this model would be to build a HMM on top of it, ...
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65 views

Expected required sample length to train a hidden Markov model

Say one wishes to train a hidden Markov model with $n$ hidden states, and (accidentally) the problem itself can be described with a hidden Markov model with $n$ (or less states). What is the expected ...
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27 views

Proper scoring rules for observations with different supports

Suppose to have a bivariate variable $z_t=(x_t, y_t)$ indexed by $t=1,2, ..., T$. Suppose now that the two components have different support, i.e. in my specific problem $x_t \in \mathcal{S}$, where ...
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What is the most appropriate method to recover words transcription from the phoneme sequence with errors?

I conduct some experiments on continuous speech recognition. After the initial application of the recognizer I have the phoneme sequence that includes some errors (three types of errors: substitution, ...
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Viterbi-like algorithm with a single observation

Consider a Metropolis-Hastings walk over an exponentially large, known state space S. The proposal/transition matrix P is known, as is the target stationary distribution. In fact, assume there is an ...
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determing states in HMM with BIC

I'm fitting a HMM to time series, for each data set I use BIC results to select the optimum number of states. In that, the BIC number is lowest and thereby indicating this model with that number of ...
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1answer
56 views

Way to train Hidden Markov Model in R with multiple sequences

i have multiple sequences for each of two states. I'd like to train a HMM with these to predict the state for unkown sequences. Here is an example for this problem: ...
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14 views

Posterior Marginal in Forward Backward

In computing Forward Backward Algorithm[http://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm], it seems they are calculating posterior. I knew Forward Backward is an algorithm of ...
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20 views

Practical Implementation of Speech Recognition HMM

I'm trying to implement a GMM/HMM for phoneme recognition where I have for each phoneme a 3-state left to right HMM model with start and end states with no emission. The emission probabilities are ...
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61 views

How do I detect state change in multivariate time series?

I have a multivariate time series . For each row in the data we have the values of inputs and a label for stability (0 or 1 ) . What are the algorithms that can detect the stability for an unlabelled ...
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Speech recognition, words out of dictionary

I'm performing word recognition by using a tradional procedure. I'm extracting MFCC features. Then I'm creating a code book in order to do vector quantization. After that, I train discrete HMM for two ...
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HMM library, different length sequences training

I'm using the Kevin Murphy's HMM library in MATLAB(http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html) There is a section called 'How to use the toolbox'. There is this example for GMM ouputs: ...
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Data requirements for building an HMM

I'm trying to describe the requirements that should be placed on data when building a Hidden Markov Model. Should the data fit the requirements for a Markov chain? That is, must it have discrete, ...
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16 views

Transition matrix in left-right hidden semi-Markov model

i'm developing a hidden semi-Markov model left-right . In a left-right model a sequence of $M$ states starts in state 1 and ends in state M, with no repetition of states. Since the model is ...
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How can you use HMMs and ANNs for on-line handwriting recognition?

I've asked this question on cs.stackexchange before. It has a 20-hours remaining bounty there. On-line handwriting recognition is the task of converting a series of $(x(t),y(t))$ coordinates to ...
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30 views

Hidden Markov process

I've been reading about hidden Markov models, and I'm interested in both discrete and continuous time models (and discrete states). I have found many papers on the discrete time HMM, but not the ...
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Concavity of log likelihood for hidden markov models

Could you give me a good link where the concept of concavity of the log likelihood related to hidden markov model EM algorithm is clarified? Thank you in advance.
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HIdden Markov model training Baum Welch, concavity log likelihood

Hi i'm developing an hidden markov model algorithm training with multiple sequences. The recognition rate is good but i have doubts about the shape of the curve of log likelihood obtained from the ...
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Example on Hidden Markov Model

I was studying Hidden Markov Model(HMM) recently. I was looking to cross check what I understood. I found a code on Forward-Backward and Viterbi is given in simple Python terms in Wikipedia. I ...
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Forward Sampling for HMM

Is it reasonable to use forward sampling to compute the probability of P(X_1=x_1, ..., X_N=x_n) in an HMM where is the observation variable? Is the forward sampling algorithm related to the ...
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Distance between a transition matrix and an instance

I am trying to put a number to the distance of a sequence and how close it is to the original training corpus. From the original training data, I got a markov transition matrix (TM). So from the ...
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46 views

How to find log-likelihood of multiple sequences for hmm using Kevin Murphy toolkit for MATLAB

I have an observation sequence of TPM, EPM and prior. I want to find the log-likelihood of around 100 sequences of length 10 at a time. How can I do this using a forward algorithm?
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Why Baum-Welch algorithm is an instantiation of EM algorithm?

$\newcommand{\E}{\mathrm{E}}$ I don't understand why Baum-Welch algorithm is an instantiation of EM algorithm. Indeed, why computing $\alpha_t(i)$ and $\beta_t(i)$ corresponds to Expectation step. ...
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152 views

Hidden state models vs. stateless models for time series regression

This is a quite generic question: assume I want to build a model to predict the next observation based on the previous $N$ observations ($N$ can be a parameter to optimize experimentally). So we ...
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462 views

Training a Hidden Markov Model, multiple training instances

I've implemented a discrete HMM according to this tutorial http://cs229.stanford.edu/section/cs229-hmm.pdf This tutorial and others always speak of training a HMM given an observation sequence. ...
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Hidden Markov Models methods for selecting optimal number of states

Package RHmm (R) I have a vector which I fit into a hmm model in an attempt to select an optimal number of states for a hidden markov model. ...
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Hidden Markov Model: Predict observation sequence from state sequence

Given a transition matrix, starting probability, means and covariances Is it possible to predict the most likely observed sequence for a given state sequence using the above details? If yes, how? ...
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HMM learning from video data?

I am having a problem understanding how to learn the parameters for the HMM from observed data. Let's say that my HMM model has one hidden variable for affect(emotion) with three values/states (anger, ...
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How tho choose the number of components in a Bayesian Hidden markov model

I'm implementing a bayesian Hidden markov model. I now face the problem of how to choose the number of components. I have two problems: 1) which index is better to use? 2) suppose i decide to use the ...
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Markov Switching and Hidden Markov Models

Are the two interchangeable terms? I have been reading about markov-switching models and am struggling to see the difference with HMM models.
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What should be done to deal with missing observations ( or outlier observation ) for Viterbi?

I want to use Viterbi algorithm, to decode an HMM sequence, but very few observations are missing in some of the steps or outliers. The hidden states in these steps are assumed to be the same as ...
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How do I train HMM's for classification?

So I understand that when you train HMM's for classification the standard approach is: Separate your data sets into the data sets for each class Train one HMM per class On the test set compare the ...
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Why are after training 5-state HMM only few entries of transition matrix left greater than zero?

I try to create the speech recognition system based on 5-state HMM + Multivariate Gaussian function. I use my own feature vector derived from MFCC (Mel-frequency cepstral coefficients). The problem is ...
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Exact inference in a Factorial HMM with 2 hidden state chains

I am trying to understand the process of exact inference in Factorial HMM models. While it is explained here (Appendix B, page 20). I think my goal is slightly different and I am struggling to fill in ...
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Confidence interval for hidden markov model (MATLAB preferred)

I'm trying to uncover the transition parameters of data of a hidden Markov Model using MATLAB. Using the built in hmmtrain function, I can estimate the parameters quite well (I already know what they ...
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How to combine states of viterbi path?

I am using HMM in my project. After Viterbi, I can get a sequence, such as 0000000000011111011111100000000000111111111111....... The 0 in subsequence 111110111111 may be wrongly decoded. Because in ...
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Markov model for time series, going back n periods?

I realise there are a lot of questions about markov here, but as we say in Dutch, I couldn't see the threes through the forest. I have a sequence of intervals between subsequent notes (pitches). ...
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Computing Standard Errors in EM algorithm

I'm applying the EM to a hidden markov chain (the $\mathbf{Z}=\{Z_1,...,Z_n\}$ variable), with observations(the $\mathbf{Y}=\{Y_0,...,Y_n\}$ variable) dependent not only on the hidden markov chain, ...
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Scaling the backward variable in HMM Baum-Welch

I am just trying to implement the scaled Baum-Welch algorithm and I have run into a problem where my backward variables, after scaling, are over the value of 1. Is this normal? After all, ...
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How to incorporate per-observation emission priors into HMM

I have a two-state HMM, in which my belief in emission probabilities depends on the observation. Basically, in addition to the two vectors of emission spectra (one for each state), I also have two ...
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The state-of-the-art methods for speech recognition?

Recently I noticed that Google and Apple have really high quality speech-recognition services. I was wondering about the state-of-the-art methods and techniques they are/might be using to achieve such ...
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Does this graphical model describe a hidden Markov model?

I'm facing the problem of visual tracking in computer vision. I have some observation (image blobs by background subtraction) produced by some moving object, and the task is to infer the state ...