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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Feature Selection for Multivariate Multilabel Time-series Classification

This is my first post and I am a beginner. I am working on a dance recognition project, where I collected skeletal data from one dancer performing 5 different gestures. My goal is to detect any pre-...
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50 views

Enforcing minimum number of observations before deciding state in HMM

I am trying to model a state detection problem using HMMs (using pomegranate library in python but that is besides the point). One of the challenges I have when using Viterbi to decide the most likely ...
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Usage of Hidden Markov Models

I have a set of questions regarding how HMMs are used. Context: there is a stream of real numbers or real number vectors (e.g. data from a phone accelerometer) and the goal is to detect that an ...
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464 views

HMM depmixS4 using a vector of known states to fit model

I am using the depmixS4 package to fit HMMs to RNAseq count data. My workflow is as follows: Stack reads into a 'stack' vector which looks like this: ...
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Hidden Markov Model: Proving E2 independent of E4 given X3

In a first order HMM (each state only depends on the previous state, and each observation $O_i$ only depend on the current state $X_i$), I want to show that $E_2\perp E_4 | X_3$. Here's a proof given ...
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How to apply HMM for each class separately in R? [closed]

I am using depmixs4 package in R to apply HMM for a classification problem. My response variable is binary. I should train HMM for each class separately. I split ...
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Normalization for MFCC?

I'm planning on using MFCCs extracted from audio signals to make a speaker recognizer. I noticed that the first MFCC term tends to be very large, compared to the others. That's why I think that ...
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136 views

How to factor covariates in a Hidden Markov Model?

Good evening everyone. I'm working on Hidden Markov Models and I mainly studied them on the Rabiner tutorial from 1989 and the book "Hidden Markov Models for Time Series: An Introduction Using R, ...
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How to simulate the hidden states from a HMM conditioned on the observed sequence?

Given a heterogenous (discrete) Hidden markov model with hidden states, $x_1, \dots, x_n$ and observed states $o_1, \dots o_n$, known transition matrices $T_1, \dots, T_n$ and emission matrices $E_1, \...
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Is there a concise mathematical form for the probability of ending up in a given state of an HMM?

I have an HMM where I know (or at least have estimated) the transition properties. I also know the starting state. I'm interested in knowing the probability that I end up in a given state "much ...
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66 views

HMM with emission depending on past states

I'm looking for a way to fit a model akin to a Hidden Markov Model, with a Markov chain of hidden states. However, the observation at each time point depends on the past ~10 hidden states, instead of ...
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What are some applications of unsupervised HMMs?

Supervised HMMs can be applied to many problems like POS tagging and OCR (optical character recognition). I've learned that HMMs can be trained unsupervisedly using EM (Baum-Welch algorithm), what ...
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Gibbs Sampling vs. Using Raw Probability in Contrastive Divergence

In Hinton's Practical Guide to Training Restricted Boltzmann Machines, Section 3, he discusses different situations in which one should take a sample from the Gibbs sampling process, and other ...
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How to initialize and train a Hidden Markov Model to improve the classification produced by a previous classifier?

Say we've previously used a neural network or some other classifier C with $N$ training samples $I:=\{I_1,...I_N\}$ (that has a sequence or context, but is ignored by C) the, belonging to $K$ classes. ...
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What significance can be attached to the eigenmodes of a transition matrix?

I am studying a continuous dynamical system and am categorising preferred areas of the system's phase space using a 3 state hidden Markov model. As the resulting transition matrix is row stochastic, ...
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'Continuous-state' Regime Switching Time Series Model?

I've been thinking of an idea, and I've had a difficult time finding any information on it so I'm not sure if there's a literature on it, or if I have an awesome original idea, or a stupid idea of no ...
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358 views

A Hidden Markov model with covariates in the transition probabilities

I would like to construct a Hidden Markov model with data about online customer journeys. A well-known concept related to the customer journey literature is the sales funnel. Consumers walk through ...
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Estimating hidden rating in games

There are a number of online games (e.g. Overwatch, League of Legends) that use a hidden rating system thats designed to be an estimate of your "true" skill level, and is used to match you with/...
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279 views

Hidden Markov model MS GARCH

I am currently working on the MS GARCH model proposed by Haas paper given below $\epsilon_n=Z_n \sigma_{\Delta_n,n}$ where $\epsilon_n$ is a time-series of residuals, $\left\{Z_n ,n\in \mathbb{Z}\...
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Problem on the stability of discrete-time Markov chains

There is a system which follows the equation: X(n+1) = X(n) - 1 + A(n), where A(n) is a random variable taking values 0,1,2 with probabilities po,p1 and p2, respectively. Now, X(n) = n and X(n)>=1 are ...
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Markov Switching GARCH conditional distribution

Given a Markov switching GARCH model $\epsilon_n=Z_n \sqrt{\sigma_{\Delta_n,n}}$ where $\epsilon_n$ is a time-series of residuals, $Z_n $ is a sequence independent and identically distributed ...
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Solution of HMM

Consider the following hidden markov model: $X_t =F X_{t-1} + e_t$; $Z_t = H X_t + v_t$; $X_t \in \mathbb{R}^n, Z_t \in \mathbb{R}^m, e_t \sim N(0,Q), v_t \sim N(0,R)$. Suppose that the process $...
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Hidden Markov Chains - Is there a typo in this lecture?

Lecture in question: https://www.cse.buffalo.edu/~jcorso/t/CSE555/files/lecture_hmm.pdf Slide #6 shows this graph: Slide #7 gives these probabilities: Is P(Dry|High) = 0.3 on slide #7 a typo?
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562 views

HMM forward algorithm in R [closed]

maybe a very basic hmm implementation question. Would there be a way to determine the most 'likely' of two different HMMs for a specific sequence? I was thinking about using the forward algorithm ...
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781 views

HMM for prediction sequences of binary vectors having fixed length

Please help me with a question. I want to use the hidden Markov model (HMM) to predict sequences of binary vectors of fixed length. For example, there are such observations: 01001 00101 10010 ...
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Why must the features be local for a Hidden Markov Model?

Assume we have an Hidden Markov Model: h1 -> h2 -> h3 -> h4 | | | | v v v v x1 x2 x3 x4 with Markov Property (can ...
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Moment conditions for MS GARCH (Haas et al.)

I am doing some research on MS GARCH, specifically the model proposed by Haas et al., however I am really stuck on the moment conditions derived in the appendix of the paper. Right before equation ($...
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Classifying mouse gesture patterns based on time series of mouse coordinates

Hint: I am a programmer, but not a machine-learning guy, so be patient with me ;) I am currently working on a side project for finding patterns in mouse movement data. Example of patterns I am ...
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120 views

Expectation Maximization for Urn Problem

I am currently investigating the following mental exercise: An urn is filled with N balls. Each ball possesses a number and it is either red or green. There are M color detectors. Each detector ...
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How do Hidden Markov Models handle spatial data?

How do hidden markov models (HMMs) handle spatial data? I've seen papers where they divide the spatial area up into a grid and then model the order of the markov chain from left to right, down a row, ...
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301 views

HMM and State Duration Estimation [closed]

State duration in HMMs follows a geometric distribution by construction. How can one estimate the state duration, since it is not explicitly modeled (e.g., in HSMMs)? I'm particularly interested in ...
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Hidden Markov Models - Weight observations

I am interested in using Hidden Markov Models in the context of multi-sensors sequences in vegetation remote sensing. The observation labels are the same as the state labels. Each observation at a ...
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MFCCs and MoG-HMMs for speech recognition

BACKGROUND MFCCs are coefficients which represent the most important parts of speech, and about 12 of them are used to model a one 512 points long frame (of speech). Along with them you would use ...
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156 views

Combining the forward and backward algorithms in HMMs

Suppose that I have a Hidden Markov Model and want to estimate the probability of a sequence of observations. Furthermore, not only do I wish to know the total probability of the sequence, but I want ...
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HMM and CRF: the label bias problem and I-equivalence

I have a question about the label bias problem in HMM and CRF. I understand that HMM and MEMM suffer from the label bias problem, which is a preference for states with fewer transitions. The problem ...
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Simple Explanation of Baum Welch/Viterbi

I'm looking for a very simple explanation as possible for Baum Welch and Viterbi for HMMs with a straightforward well annotated example. Almost all of the explanations I find on the net invariably ...
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What if transitions and emissions in a hidden Markov model are not independent?

A Hidden Markov Model is given by transition and emission matrices. The transition matrix determine probability of "next states" as a function of the current state. The emission matrix determine the ...
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Hidden Markov Models with two emissions per state

I have a problem formulated in terms of hidden markov models. A simplified version is discussed here. I have a system that transitions between two discrete hidden states (states 1, 2). The transition ...
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Prediction using Hidden Markov Models

I am trying to model financial time series data using Hidden Markov Models. This question is related to time series analysis in general. Can I create the model on previous few days data and use that ...
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419 views

Markov chain and withdrawing balls from a box without replacements

I have a box that contains three types of balls: blue yellow and red. I do not know how many balls there are in the box nor the proportion of them, the number of the balls is not infinity though. ...
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239 views

Overlapping Gaussian output distributions for HMM states

The emission probabilities of a 2-state HMM model have overlapping Gaussian distributions with equal mean values. If the observed data sequence X is given, is it possible to infer the state sequence ...
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how can i use an HMM to match two sequences when either could have junk rows at the start?

I have a target and observed list of names. Both are in the same order but there are some missing from each list. I am using an HMM to match the two lists and it works very well in most circumstances. ...
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Computing Classification Error between HMMs

Consider two Hidden Markov Models. They have different state space but have the same output space and are all left to right HMMs. Is it possible to compute in closed form the probability of a sequence ...
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360 views

Hidden Markov Models as Dynamic Bayesian Networks

I'm working on a project where I'm trying to classify violent events into different latent states (e.g., low, medium, and high). Each series is distributed Poisson and I'm controlling for population ...
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601 views

Many values of HMM matrices, A and B, tend to zero

I'm experimenting with an HMM. I have a sequence of observations (10000) and the original matrices A,B and pi that generated those observations. There are 4 types of observations. What I am trying to ...
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HMM with unknown number of hidden states

The Baum-Welch algorithm can estimate the parameters for a given structure of a HMM. However, how to determine the structure, specifically the number of hidden states? Ideas so far: Trial and error, ...
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Is there any relationship between Naive Bayes and Hidden Markov model?

Is there any relationship between Naive Bayes and Hidden Markov model? Can we derive one from another?
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Baum-Welch and hidden Markov models: Continuous observation densities in HMMs

I am currently trying to understand how parameter are being reestimated for hidden Markov models (HMMs), using expectation-maximization (EM). What I seem to have problems understanding is what the ...
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598 views

HMM for multichannel - multivariate data

I have a dataset which contains multiple time series of multivariate data both continuous and categorical I would like to use a Hidden Markov Model (HMM) to find the number of hidden states for all ...
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107 views

Is there any specific use of Hidden Markov models in physics?

I have just discovered what is a HMM and I think it has pretty good predictive power and I thought whether they can be used in any specific branch of physics.

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