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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Supervised HMM Training in R

I'm looking for an R package that implements the supervised training of Hidden Markov Models. By supervised, I mean given a sequence of observations, we know which ...
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Build HMM of text data in R

I'm trying to make my own HMM tagging in R but don't know how to estimate parameter values since the packages I have been working with haven't worked with my data. The latest package I have been ...
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Hidden markov implimentation problem. having trouble using hmmtrain in matlab [closed]

I am having trouble with a hidden markov model using the hmmtrain function in matlab. I have an observation sequence (seq) that is 349 observations long with symbols ranging from 1 to 12. I have an ...
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Explain Backward algorithm for Hidden Markov Model

I have implemented Viterbi and Forward algorithm, alas strangely I can't understand how does Backward algorithm work. Intuitively I feel like I need to do the same thing as in Forward only backwards, ...
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68 views

Correlated features in dataset

I undestand in general that it is important to take correlational structure into account while applying almost any statistical techniques. First question - could you help with the examples why it is ...
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Is the following a standard HMM variant?

I have a problem that looks to me like a HMM variant. Could somebody confirm that I am on the right track modelling this and possibly tell me the name of this HMM variant if it is a standard HMM ...
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Understanding a paper on a modified HMM

Link to the paper here. In section 3.3 the authors define their modified Hidden Markov Model, which I don't quite understand. My main question is when coding the Baum Welch algorithm for this HMM ...
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8 views

Music Genre Classification using combined SVM and HMM

We are doing a Music Genre Classification software using C#. We want to know if combining HMM and SVM as a classifier, by using the distance of trained data on the hyperplane as an input sequence for ...
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18 views

Continuous HMM library/Tool for continuous gesture recognition

There are many HMM tools, but most of them only support discrete density HMM (like Matlab). even the tools that support continuous density HMM can only be used for discontinuous gesture recognition. ...
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emission matrix in hidden markov model

I'm using a Hidden Markov Model for fraud prediction in credit card. I have already created the transition matrix using data from a set of training data data in term like this LLMHLHLMMLHH. I can't ...
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how to write down dynamical state space models with deterministic variables in PyMC?

is it possible to write down this simple dynamical system in pymc? $R_0 \sim Normal(\mu_r, \sigma_r)$ $Z_0 \sim Normal(\mu_z, \sigma_z)$ $R_t \sim Normal(R_{t-1}, \sigma_r)$ $Z_t = Z_{t-1} + ...
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How do you factor covariates into your hidden markov model?

I am currently implementing a Hidden Markov Model in Python and would like to add covariates to my model. There are 2 categorical covariates each covariate can take up to 4 values. For each ...
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HMM Model Selection

What is the process for selecting a model for an HMM? Say the data is time sequences, where each time sequence represents a class. I can used Baum-Welch to train, but I don't know how to determine ...
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Hidden Markov Model prior

Suppose I have a set of time sequences, and that each time sequence is representative of a class. I can train several HMMs using Baum-Welch, one HMM per class. Let all HMMs have the same number of ...
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Is it possible that maximal probabilities in hidden markov model are same for all sates?

I developed forward/backward algorithm for calculating probabilities for each state in hidden markov model and I got that maximal probabilities are the same in final probability matrix. This is data ...
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21 views

Classification using HMM

I would like to use HMM to classify a sequence of alphabets while preserving their orders. Just to put it formally, I am looking to compute $p(c|x_{1},x_{2}\ldots, x_{n})$ where ${x_{1},x_{2}\ldots, ...
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24 views

Hidden Markov Models and Viterbi Algorithm: Fair and Biased Die

So following is the problem that I am trying to solve using Viterbi algorithm and HMM: Before attempting to write a program, I want to do this problem by hand for the first 3 observations($651$). ...
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54 views

Hidden Markov Model and Viterbi algorithm: Understanding the Casino Problem?

I am deeply struggling with understanding how to apply the Viterbi algorithm. From my course notes, I have the following simple(I'm told) example: If the sequence ...
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How do I consider the whole training set in the process of Hidden Markov Model training?

I'm trying to train a Hidden Markov Model following theory from the book "Pattern Classification" by Duda, Hart, and Sotrk. For the HMM learning they discuss Forward-Backward algorithm there, ...
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106 views

Deep Learning - What are the senones in a Deep Network?

I am reading this paper: skype translator where they use CD-DNN-HMMs (Context dependent Deep neural Networks with Hidden Markov Models). I can understand the idea of the project and the architecture ...
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190 views

Difference between Hidden Markov models and Particle Filter (and Kalman Filter)

Here is my old question I would like to ask if someone knows the difference (if there is any difference) between Hidden Markov models (HMM) and Particle Filter (PF), and as a consequence Kalman ...
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Terminology of “Polytree graphical models” in the context of graphical models, HMM, Naive Bayes

I'm reading a paper in which they are attempting to balance between cost of obtaining information and information value in graphical models. At one point they talk about chain graphical models such ...
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28 views

Initializing hidden Markov models (HMM)

I am developing a system that uses hidden Markov models to recognize hand gestures however I am unsure of how to initialize the transition ($A$), emission ($B$) and initial condition ($\pi$) matrices. ...
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Baum-Welch with additional data

I have a HMM and multiple observation sequences possessing different initial state values. My observations include lots of data beyond the observed output (features I could use when training ...
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Can Markov chain being used as sequential data classifier instead of Hidden Markov Model?

I was finding algorithm that can classify sequential data into categories. Let's say sequence '000111' falls into category A, and '01010101' falls into category B. I found many works similar to my ...
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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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26 views

HMM - which are possible hidden states and observational states in this scenario?

I have read about HMMs including an weather example and how to apply them on credit card fraud detection. Now I want to apply HMMs to detect credit card fraud on an ATM transaction. I am confused as ...
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Predicting Trend using Hidden Markov Model

I am trying to predict exchange rates trend using Hidden Markov model (Viterbi Algorithm). For 2 state trending, I use A = [0.9 0.1; 0.1 0.9] as transition matrix and B = [1/N 1/N ....1/N; 1/N 1/N ...
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Estimating Hidden Markov Model (HMM) emission probabilities from POS data in R

I have a POS-tagged data set. ...
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36 views

Applying HMM to time series data

I have times series data from accelerometer that was attached to a person that was doing different type of exercises. I have a feature matrix that is basically a table with 3 columns (3-axis ...
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What type of model am I looking for?

I will explain by way of an example. Say I have data on 2 groups of people (boys and girls). I would like to learn 1 model for girls, 1 model for boys. The variables to consider are things like what ...
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43 views

Understanding elements that characterise HMM

I am tryign to understand HMM based on this paper: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition but I find it difficult to actually understand one of its basic ...
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1answer
53 views

Semi-supervised method for identifying states and state durations in a time series for anomaly detection

I am developing a semi-supervised method for identifying anomalies in a time series with multiple states. Let's consider this example time series in which there are two states e.g. state 1 and 2 with ...
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unified estimation of discrete Markov Model

Background I have a multivariate dataset, say M x N, where M is the number of variables and N is the number of samples. Now, the pattern of dependencies between the M variables changes across the N ...
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Speech recognition using baum welch and GMMs

I have been working on a speech recognition program for a while now, implemented in java, it uses HMMs as structure model and Baum welch for training using gaussian mixture models. Features are ...
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Modeling multiple time series functions

Consider $X_i[n]$ as data recorded for time instant $n$ and the day $i$. I am interested in finding a good model for my time series analysis that includes both the parameters $i$ and $n$. In my ...
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chicken egg problem

I should run a test to determine whether variable A affect variable B or viceversa. What type of test should I use? Imagine that we measure the time slept per night (A) and the activity performed ...
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128 views

How to train a Gaussian mixture hidden markov model

I want to build a hmm with continuous observations modeled as Gaussian mixtures. The way I understand the training process is that it should be made in 2 steps. 1) Train the GMM parameters first ...
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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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100 views

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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35 views

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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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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152 views

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: $P(x|z_0,...z_{k+1})\propto P(z_{n+1}|x)P(x|z_0,...,z_k),\,k=0,1,\dots$. $P(x|z_0)=P_0(x)$ ...