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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EM algorithm maximize which objective function

In Wikipedia EM algorithm section of Gaussian Mixture examples , there are two likelihood functions: incomplete-data likelihood function $L(\theta;\mathbf{x})$ complete-data likelihood function $L(\...
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Which Algorithms can model a sequence and predict the next value of this sequence

I have a repeating sequence like the following, with occasionally random values (maybe noise): ABCDABCEDABCDABCD What could be an algorithm to model the sequence and predict a following value at a ...
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Is Hidden Markov Model suitable?

I have the following problem I consider to model with HMM. However, it seems to me like a non-standard application or it possibly makes no sense employ HMM. I already have some practical experience ...
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What algorithm would be appropriate to find nearest neighbors based on transaction history

I have two data sets with transaction history of customers by date and product (de-identified). These are from two different sources and have different capture rate (e.g.: One might have 5 ...
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1answer
72 views

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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300 views

Non Markov Processes and Hidden Markov Models

Is it possible to model a Non-Markov process using Hidden Markov Models? In other words, can we look at the hidden states as the memory of a Non-Markovian system? Thanks.
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Deep Learning for sequences

I want to use deep learning techniques to perform better inference tasks than Hidden Markov Models (which is a shallow model)? I was wondering what is the state-of-the art deep learning model to ...
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Event prediction: Which messages come together and will come when in the future?

Disclaimer: I am pretty new to time series prediction. I hope my questions are not stupid. If so, or they should be changed, give me a friendly nudge. Tasks I have a system that gives me status ...
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Why do (hidden) markov models have strong predictive power?

(Hidden) markov models predict the next future state only dependent on the current state. I would not expect any algorithm that is only considering the present state to have a lot of predictive power. ...
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672 views

Decode the most likely sequence of states for the following sequence

A two-state HMM is constructed from the measurements shown below. The mean length indicates the average time that the HMM stays in the state. Decode the most likely sequence of states (P/Y) for ...
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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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866 views

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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Matlab hmm viterbi algorithm calculated state sequence Probability

I have used matlab hmmviterbi function in my code for calculating the most probable state sequence from observation sequence. Is there any way to derive the probability (score) of this calculated ...
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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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HMM: Why are observations conditional on the latent state and not vice versa?

The model of a HMM consists of a latent Markov chain with state $X$ and transition probabilities $P(X^t \mid X^{t-1})$, and observation variables $Y$ that depend on the current latent state via $P(Y^t ...
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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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1answer
432 views

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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1answer
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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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390 views

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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1answer
163 views

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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1answer
709 views

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: ...
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Difference between MLE and Baum Welch on HMM fitting

In this popular question, high upvoted answer makes MLE and Baum Welch separate in HMM fitting. For training problem we can use the following 3 algorithms: MLE (maximum likelihood estimation), ...
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Find the expected value of the observation at time T+1 in HMM

I have built a HMM model in R using depmixs4 package. I have the state projections and posterior probabilities till time T. I understand that to find the state ...

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