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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HMM with final or absorbing state

I am reading about HMMs and it's unclear to me if they are required to have a final state. In particular I have seen examples of HMMs that have an absorbing state and other examples with no final ...
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Can anybody tell me the process of finding the correlation using HMM [on hold]

I want to extract the correlated data in the given data set. can anybody help with a step by step procedure of evaluating this
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Multiple continuous observations in HMM gaussian mixture

I am building a hmm which emits 3 types of continuous observations. I intend to model each observation sequence as a vector in $R^3$. I am new to hmm and my first question is can I use a tuple or ...
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Statistical model to predict the next move on network only using movement history

Is it possible to build a statistical model that predicts the next move in a graph solely based on past movements and the structure of the graph? I have made an example to illustrate the problem: ...
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Change detection in hidden markov models

I have many questions about hidden Markov models. Let $Z_1$, $Z_2$, ..., $Z_n$ be the latent variables, and $X_1$, $X_2$, ... $X_n$ be the observed ones. Let's assume that the parameters of the ...
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Pros&Cons of Hidden Markov Models in Time Series Forecasting

What are the advantages and disadvantages of Hidden Markov Models in forecasting values of a time series (compared to other methods, e.g. ARIMA)?
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fit a HMM model to a sequenc

I am learning HMM... what is the way to fit a HMM model to a sequence? for example, I have the seq=[1 3 2 4] made of 4 symbols and the number of hidden states is N How to do it using Matlab? 1) ...
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Why isn't a gaussian mixture prone to overfitting?

Consider a Gaussian mixture of 2 components and a dataset of size $N$. The EM algorithm use the data to estimate: the model parameters: the means $\mu_1, \mu_2$ (say the covariances matrices are ...
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HMMs with feature vectors (block HMMs?)

I'm quite new to HMMs, but still couldn't find an approach to fit HMMs in R, where we have feature vectors instead of single values. Or perhaps I simply didn't understand some of the proposed ...
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Filtering distribution for HMM output

I have an HMM with both a continuous and a discrete outputs. This discrete output, let's call it $y_t$ is deterministic but depends not only on the hidden state $z_t$ but also on its previous value ...
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Why transitions and emissions in HMM are assumed to be independent?

In the hidden Markov model we use two matrices. The first one, called transition matrix, determines probabilities of transitions from one hidden state to another one (the next one). The second matrix, ...
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Calculating future states

I am working with HMM to predict the future states of a sequence. Using forward algorithm I can calculate following probability. And I need a way to calculate the prediction probability; for an ...
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HMM hidden markov model starting point

I am novice with hidden markov models. What is the minimum starting point to implement a hidden markov model. I mean, what it is necessary to know a priori?. I know in hidden markov models the states ...
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What is the limiting distribution of the Bayesian Filtering

I've got a question about the iterative Bayesian filtering, the general form of which is shown as follows: $P(x|z_0,...z_{k+1})\propto P(z_{n+1}|x)P(x|z_0,...,z_k),\,k=0,1,\dots$. $P(x|z_0)=P_0(x)$ ...
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forwards algorithm - derivation

I am self-studying hidden markov models, and am struggling to with the derivation of the forward algorithm, and especially the definition of $\alpha_t$ as the hadamard product. It would be much ...
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Hidden Markov model - formulas

I am self-studying Kevin Murphy's book, and trying to understand the math behind the HMM. I am struggling with the following derivation. Why can we in 17.46 cross out the $X_{1:t-1}$ term? my guess ...
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Relation between AR(1) and Vasicek model

The discrete time version of a Vasicek model is equivalent to an AR(1) model with opportunely chosen parameters, as showed in this paper: http://www.damianobrigo.it/toolboxweb.pdf. Following this ...
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Real Data Sets Examples

I have a set of observations $\mathcal{Y} = {Y_1, \cdots, Y_T}$. I am running EM algorithm to fit the observations to the following Hidden Markov Model $$A = [a_{ij}]_{N \times N}, a_{ij} = P(X_{k+1} ...
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Calculating BIC for a HMM without any training data

I need to evaluate my HMM models, that are trained using EM algorithm, since I don't have any training data. In order to evaluate with BIC or with most of the other criterions I need the log ...
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Varying transition probabilities by position

I'm still very new to Bayesian Tables, Hidden Markov Models and the likes, but have an otherwise solid computational and linguistics background. I've been diving into NLTK (Natural Language Toolkit) ...
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What software estimates hidden Markov models?

I am trying to estimate a hidden Markov model (HMM) for a research project. Suppose that I observe a sequence of emissions, but I do not know the sequence of states the model went through to generate ...
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best offtheshelf MATLAB hidden markov model toolbox

Whats the best,current off the shelf hidden markov model toolbox to use for MATLAB? I came across this http://bnt.googlecode.com/svn/trunk/docs/usage_dbn.html but it seems a bit old. there are ...
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A question on mixture model

I'm a bit confused by the conception of "mixtrue model" I'm studying hidden markov model, which is frequently referred to as a "mixture model". But I don't know what the term "mixture" implies. ...
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How do I use Hidden Markov Model Viterbi algorithm for sequence labeling?

with my current small experience of HMM. Given that i have some patterns (sequence of interest for example gestures or words in spoken language) if i need to use HMM for sequence classification ...
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Test cases for HMM implementation

I've written some simple code to fit Hidden Markov Models with discrete emission probabilities (similar to this implementation, which follows Rabiner's tutorial very closely). I'd like to extend my ...
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difference between forward backward, alpha beta, sum product, baum welsh

For Hidden Markov Models, what the difference between forward backward, alpha beta, sum product, baum welsh?
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How is prior knowledge of letter/word patterns incorporated into handwriting (or speech) recognition?

Using handwriting recognition as an example, we can train various models to recognise individual characters but to actually be useful we must incorporate prior knowledge of common character sequences, ...
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How to reestimate GMMs in a HMM-GMM

Context: Automatic Speech Recognition I understand the training of a pure HMM with Baum-Welch: Expectation step compute $\gamma_t(i) = P(q_t=i |O,\lambda)$ //p(passing state $i$ at frame ...
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How can I train HMM for continuous sign language recognition?

Currently I can recognize isolated words using HMM (Hidden Markov Model) through training an HMM model for each sign, and for a new word I take the sign for the model giving the highest likelihood. ...
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How is $\sum\limits_{t=1}^{T-1} \xi_t(i,j)$ the expected number of transitions in Baum Welch

Context: Baum-Welch Algorithm, Maximization step Serveral tutorials, e.g. this one say that $\sum\limits_{t=1}^{T-1} \xi_t(i,j)=\sum\limits_{t=1}^{T-1}\frac{P(q_t=i, O | \lambda)}{P(O|\lambda)}$ can ...
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hmmtrain default prior vector

I am using hmmtrain as follow: [trans,emiss]=hmmtrain(symbol_seq,trans0,emiss0); My question is: What is the default prior vector (pi) that used in hmmtrain.m ...
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Sensitivity to scaling of multivariate data with HMM

I have some multivariate data, say 40 features. Some features are scaled between 0 and 1, and some are scaled between 0 and 1e8. For reference, I am using sci-kit learn's HMM implementation (yes, I ...
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Connection between Hidden Markov models and logistic regression?

This question is inspired by a comment below this question on Hidden Markov models: "Have you considered logistic regression? For non longitudinal data, they are practically the same thing." My ...
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Material on plate notation of bayesian hidden markov model

Does any one know some materials on plate notation of Bayesian Hidden Markov Model? Say, given multiple observed sequences, how to infer the posterior distribution of the parameters, and the ...
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hmm variable number states

I have looked around for a while but cant seem to find any literature on this but can't seem to find a treatment. I have a set A of observations at time $t_n$ $A = \{A_1... A_n\}$ Further I know that ...
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Ttests for DNA sequences

Let's say I have two sets(1000 sequences in each set: set1 and set2) of independent DNA sequences of length 30. I created a HMM model by using DNA sequences in set1 which calculate Prob(Seq/Model). It ...
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Impulse Response Analysis for Markov-Switching Models

I've estimated a bivariate Markov-Switching VAR(1). I am interesting in understanding how an error/ 'shock' would propagate through the system. I've been stuck on how this could be done? Thank you!
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Markov models that with several active states

Are there any Markov-like models that can have several active states? So say if trying to determine (the chance) when the person will wake up based on two variables (weather and the time the person ...
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How to choose between VOMs and Predictive models, e.g., ARIMA?

In time series prediction, there is a lot of work that uses predictive models (e.g., ARIMA). On the other hand, there's also a lot of work that uses Variable Order Markov models (e.g., context ...
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Reference request: EM algorithm and hidden Markov model books with solutions

I am studying missing data problems and the applications of the EM algorithm to missing data problems, like mixture models and hidden Markov models. We have been using Schafer's book Analysis of ...
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What are good tutorial on Weighted Finite Automata?

I would especially appreciate papers, books or tutorials with source code already available. Currently I'm reading "Spectral Learning Techniques for Weighted Automata, Transducers, and Grammars" by ...
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Hidden markov model multivariate regression with time-series data

I am working with a dataset that includes the trajectories of various car trips and would like to be able to predict their destinations using only a subset of the trip trajectory. For instance, if in ...
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How to use a Hidden Markov Model to detect state in a time series?

Questions Am I right in assuming that the emission probabilities will not be following a gaussian distribution for my particular problem? Obviously, I will need to train the model for state ...
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disease progression R markov chains

hello i have a dataframe, some of the columns include: remission,height,weight,time from diagnosis, age ethnicity, age, patient id remission is 1 or 0 just to be clear i want to fit an appropriate ...
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Markov chains vs. HMM

Markov chains makes sense to me, I can use them to model probabilistic state changes in real life problems. Then comes the HMM. HMMs are said to be more suitable to model many problems than MCs. ...
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Estimate core state transition probability matrix in partially observable Markov decision processes

I have a longitudinal data set of patients who have been monitored by a medical test over a year. The results of this test have false positive and false negative, so the system is partially ...
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Implications of using fixed effects to account for hierarchical data structure

I am currently implementing a hidden Markov model in R, using the msm package. The data I am using are drawn from a cluster-randomized trial; i.e. there is a ...
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Hidden Markov model question; pseudo time series?

I apologize that the title of this question isn't super specific, but I am having a very difficult time exactly and succinctly describing the problem I am facing in my implementation of a hidden ...
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Implementation of bag-of-features HMM?

I'm trying implement a word-spotting paper that uses a Hidden Markov Model where the emissions are a bag of words/features. I have found HMM implementations, but I haven't seen functionality to ...
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time series based classification

I want to classify some data. Basically the data is time series in nature. The target variable is categorical. I know there are so many algorithms for predicting the time series model. However, I have ...