# Questions tagged [hidden-markov-model]

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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### what are hidden states in HMM based language model?

There are several ways to build language models, n-gram based models are straightforward, but for the language models built on HMMs, what are hidden states and what are observations?
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### Apply HMM on a stock dataset or any other real dataset

Can someone explain how to apply an Hidden markov model on a stock dataset which has many rows and columns. I am new to HMM but have been going through it for a week. Here is a snapshot of dataset: <...
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### Notation Question about Murphy's Machine Learning Textbook

I am currently on chapter 17 about Hidden Markov Models and Kalman Filter, but I am a bit confused over the notation that he uses. For instance on page 608, he uses the notation for prediction using ...
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### What is the difference between a Hidden Markov Model and a Mixture Markov Model?

From what I understand, Hidden Markov Models are those that relate observable and unobservable states, whilst Mixture Markov Models are techniques to cluster sequences according to which Markov model ...
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### Methods to validate a hidden markov model

What specific metrics should one use to evaluate the performance of a hidden markov model where the training is unsuperivsed? In essence how does one cross validate a model correctly, with regards to ...
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### HMM & the unfair die problem?

I would like to know what the right strategy is for this problem. Consider a set of 3 unfair die. Each die is rolled for a period, and then another die is randomly chosen (with replacement). All we ...
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### How do I capture the distribution of a discrete random variable signal in a mixture model?

A signal of unknown length with observations [0,1,2,3] is found in a lengthier chain of values. There is some uniform distribution for noise in the background that is known, however the probability ...
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### Sampling Hidden Markov Model

I am studying hidden Markov models, but I have some doubts about the inference phase. If I have any observations and I want to know the three parameters that characterize the model, can I use one of ...
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### hidden markov model with multiple factors

I am reading about hidden markov models. The example I have been reading is based on determining the average annual temperature on the earth over a series of years before thermometers were invented, ...
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### How to calculate likelihood from Bayes' theorem without the normalisation?

I often see in the books that the Bayes' theorem is used without the normalisation denominator as: $$P(A\mid B)\propto P(A)\cdot P(B\mid A)$$ While I understand the reasoning behind it (the ...
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### markov chain model

How to generate the transition matrix and predict the next 2 Events using Markov Chain model ? I have the data in the form shown below ...
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### how can I convert a negative log likelihood to likelihood? [duplicate]

how can I convert a negative log likelihood to likelihood between 0 and 1 ? I use HMMs package in R and I keep getting strange results of the log likelihood for example, -48569 ! I need to ...
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### Time series forecasting with Markov Chain, Markov Switch etc [closed]

I have a data set which contains closing prices of a stock every day (total 1 year). Can i forecast that set like 1 or 2 year with using Markov methods? If yes, then how?
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### Complex nested time-series ARIMA mode

This question has to do with the underlying statistical methodology. I have some expert knowledge of the domain but I am not certain the method I want to use is statistically sound. Let's assume that ...
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### What are some alternatives to Hidden Markov Models for Part of Speech tagging?

I wanted to build a POS tagger but found the HMM Tagger to be too mainstream. Any other Classifier/Statistical Model that I could use?
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### Determining number of states for Discrete Hidden Markov Model used as Classifier

I want to train a discrete HMM on two different datasets containing user clickstream data, to classify new user sequences. The datasets contain 41 different observations (41 different API calls), and ...
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### Hidden Markov Model vs Recurrent Neural Network

Which sequential input problems are best suited for each? Does input dimensionality determine which is a better match? Are problems which require "longer memory" better suited for an LSTM RNN, while ...
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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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### Implementation of Forward Backward Algorithm

While studying about Forward Backward Algorithm, I came across the question below. I was unable to solve the question after trying for a lot of time. Consider a two-bit register. The register has ...
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### Undirected graphical models with for discrete variables with hidden nodes - loglikelihood (The elements of statistical learning)

I don't understand the equation of loglikelihood of the observed data in graphical models with hidden nodes that appears in "The Elements of Statistical Learning" (Hastie, Tibshirani, Friedmann, ...
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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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### Bayesian: Joint Smoothing Recursion

Note: I am dealing with a state space model/hidden Markov model, where process $x_t$ is only dependent on $x_{t-1}$ and $y_t$ is only dependent on $x_t$. \begin{equation} \begin{split} p(x_{0:t}|y_{...
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### Forced alignment HMM

I am currently trying to understand what is involved to train a Hidden Markov Model (HMM) with Forced alignment. Forced alignment, as far I understand, is to align the audio file with the utterance ...
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### How can i estimate the covariance for Gaussian HMM?

I am trying to train the gaussian Hidden Markov Model (HMM) for my thesis problem. I am exteding hmm code from the guyz implementaion Here is the code to reestimate mean and co-variance. ...
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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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### Objective to Train HMM Maximize Likelihood

An Hidden Markov Model can be utilized to model a seq of face gestures(e.g., left eye-brow up, lip pull down, right eye closed) defining a facial gesture. I want to determine an objective to train a ...
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### Hidden Markov Model Semantics

I've been using a program that utilizes an HMM to predict the locations of gene coding regions on a DNA sequence. When writing about it, I want to make sure that I have the terminology correct. The ...
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### Interpreting output of HMM

I have a 3 state HMM model ("state1", "state2", "state3"), with an alphabet of "1" ("hit") and "0" ("miss"). Here are the parameters (these are just for example's sake) that this HMM is defined by: ...