Questions tagged [hidden-markov-models]

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Applying Baum-Welch to multiple observed sequences iteratively

When using the Baum-Welch algorithm to train a hidden markov model you normally repeat it on some observed sequence iteratively until your values converge. If you have multiple observed sequences, ...
felher's user avatar
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Viterbi algorithm for object tracking

I want to solve a problem of object tracking along time. The problem is - I have a sequence of images, and I need to find and track the creation of the objects, than their movement, and than their ...
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In Viterbi's algorithm what is the difference between the observation space and the sequence of observations?

Wikipedia has an explanation for the viterbi algorithm, in particular it describes the following summary of the inputs: What is the difference between O and Y? Also I am trying to understand what the ...
Makogan's user avatar
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Find a transducer that maps a given deterministic process to another

Let $S$ denote a deterministic process which generates a certain string, described through a Hidden Markov Model. More specifically, for a process with alphabet $\mathcal{A}$ and $n$ hidden states, ...
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Shortest path given correct order of colours?

I have a directed graph $G=(V,E)$ where each vertex is a 4-D coordinate $v: (x, y, z, c)$ representing spatial coordinates $x, y, z \in \mathbb{R}$ and the non-physical parameter colour $c \in (c_{1}, ...
batlike's user avatar
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Compare Hidden Markov Model's sample with ground truth data

I have a time-serie and I fit different HMMs on it, each with a different number of hidden states. Now after sampling from the models , I'd like to compare the results with the ground truth data and ...
Gerardo Zinno's user avatar
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468 views

What is difference between Hidden Markov Model and Non-deterministic Finite-State Machine?

As the question implies, can you mention any difference between Hidden Markov Model and Non-deterministic Finite-State Machine? Are they different or the same?
Commander's user avatar
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Hidden Markov Models for Hand Gestures

I am completing a final year project for hand gesture recognition using Hidden Markov Models I have a fair understanding of Hidden Markov Models and how they work using simple examples such as the ...
Dylan Joseph's user avatar
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Baum-Welch Algorithm

I was reading the book by Jurafsky and this is written by the author on HMM Although in principle the forward-backward algorithm can do completely unsu- pervised learning of the A and B parameters, ...
Black Jack 21's user avatar
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Is there any toolbox for Markov Random Field Structure Learning?

I need a toolbox or software that takes a dataset as input, detect independencies among its random variables and produces the relative Markov Random Field graphical structure from that. Can anyone ...
Masih Zaamari's user avatar
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Find consensus trajectory of how a genetic algorithm solves an optimization

I have implemented a genetic algorithm to find the evolutionary outcomes of a biological scenario. I simulate the evolution (i.e. optimization) of five traits in my model. I ran my code 100 times and ...
Armin Dadras's user avatar
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Prior probability in HMM

This is the HMM model considered in the question And this is the emission probabilities for the respective states. There are two emission values, bringing an umbrella and not bringing an umbrella. ...
Amey Meher's user avatar
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Performance of smooting vs viterbi algorithm with HMMs

To experiment, I implemented a discrete HMM; the transition matrix and emission model are randomly, uniformly generated. Then, a sequence of random states and emissions are produced by the HMM. Then I ...
Jack Grundy's user avatar
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Hidden Markov Model with empty states

I am using a Hidden Markov Model with Gaussian mixture emissions to cluster a sequential data (I am using hmmlearn in python 3). Initially, I used the log likelihood to find the number of clusters and ...
dduque's user avatar
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Viterbi Algorithm: initial state with ONE probability

The Viterbi Algorithm can be used to calculate the most likely path, based on observations in a Hidden Markov Model. Using the same notations as Wikipedia, "each element ...
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HMM Baum-Welch training and pruning

I am working on a HMM tagger that should be initialized with some small data and then supposedly improved with Baum-Welch algorithm on the data. However, the number of states is huge, almost $459^2$, ...
cubeception's user avatar
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HMM tagger - Baum Welch training

I am trying to implement a trigram HMM tagger for a language that has over 1000 tags. In my training data I have 459 tags. Now if we consider that states of the HMM are all possible bigrams of tags, ...
grahamGoat's user avatar
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Examples of difference between Hidden Markov Model and Bayesian Network?

I am trying to more deeply understand the difference between Hidden Markov Models and Bayesian Network? The general idea is that HMMs have a single variable which has probabilities of entering ...
Tyler Durden's user avatar
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multiple sequence alignment using HMM and simulated annealing

Can anyone help me with Multiple Sequence Alignment (MSA) using Hidden Markov Model (HMM) by giving an example or a reference except these 2 references: 1-Eddy, Sea.R., et al.Multiple alignment using ...
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counteracting numerical instability in HMM training

I am training a HMM with Baum Welch for part of speech tagging. I am training the model with 79 hidden variables (part of speech tags) and 80,000 observed variables (words). I am working with log ...
lo tolmencre's user avatar
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Hidden Markov Model initial probability reestimate: Why $\pi^*_i = \gamma_i(1)$ instead of $\pi^*_i = \frac{\gamma_i(1)}{\sum_{j = 1}^N \gamma_j(1)}$

In the sources I consulted it states that in the Baum Welch algorithm the reestimate of the initial probability of state $i$ of the HMM is $\pi^*_i = \gamma_i(1)$. But $\gamma_i(t)$ is the probability ...
lo tolmencre's user avatar
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Machine Learning Algorithm Recommendation For Sensor Data [closed]

I would like to classify data coming from a sensor. In the literature Hidden Markov Model and SVM are used, but I would like to improve results with another methods.The picture how data and classes ...
Reiso's user avatar
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Convergence of Markov model

I was learning Hidden Markov model, and encountered this theory about convergence of Markov model. For example, consider a weather model, where on a first-day probability of weather being sunny was 0....
Mihir Thatte's user avatar
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541 views

Continuous Observation Densities in HMM

I've been reading about hidden Markov models and stumbled upon A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition by Lawrence R. Rabiner (Proc. IEEE, 77(2):257–...
Allen Huang's user avatar
1 vote
1 answer
789 views

Training a HMM with Baum-Welch gives different results across runs

I am running a Baum-Welch HMM algorithm (in R). The sequence vector contains a series of observations which have been gathered from a dataset where the data has 17 states. I can successfully run the ...
user3745220's user avatar
3 votes
1 answer
370 views

How do POMDPs and Dynamic Influence Diagrams differ?

To give some perspective, first consider the following diagram comparing Markov Chains, HMMs, MDPs, and POMDPs (I'm not sure who to credit for it). Fully observable ...
Erik M's user avatar
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2 answers
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Machine learning algorithm(s) for recognizing simple graph patterns

I generate some simple graphs based on usage stats of a website, and they may look like these: I call the 'pattern' on the left 'convergence', and the 'pattern' on the right 'divergence'. The terms '...
skyork's user avatar
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What is the meaning of the output weights of a Conditional Random Field (CRF) model?

Problem When train my linear chain CRF with annotated observations, I feed it with a number of sequences containing observation values and a "ground-truth" label for each observation. I'm currently ...
s1lence's user avatar
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1 answer
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Build Automatic Speech Recognition (ASR) from scratch [closed]

I want to build a Automatic Speech Recognition (ASR) engine for myself, but I've no idea from where to start. I've read that most ASR's are build upon Hidden Markov Models, but also I've read that ...
0xdeadcode's user avatar
8 votes
2 answers
269 views

Why are HMMs appropriate for speech recognition when the problem doesn't seem to satisfy the Markov property

I'm learning about HMMs and their applications and trying to understand their usages. My knowledge is a bit spotty, so please correct any incorrect assumptions I'm making. The specific example I'm ...
sooniln's user avatar
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486 views

How to properly solve this Hidden Markov Model problem?

I got a an exercise problem which should be seen as a HMM scenario and argument some statements. However I'm quite confused about how to properly solve and argument my solutions. Problem tells: ...
diegoaguilar's user avatar
5 votes
2 answers
1k views

Combining multiple HMM models

Is there any way to combine multiple Hidden Markov Models trained from different sets of data? For example, I want to detect the phases of a sequential activity. I collect two sets of data by using ...
askingtoomuch's user avatar
2 votes
1 answer
437 views

Bayesian Nets & Markov Blanket

As i passed PHD entrance exam, some days ago, i want to find solutions for challenging problem. In Bayes network on X={X1,...Xn} each random variable has P parents and Q child's. for Xi we want to ...
user3661613's user avatar
2 votes
1 answer
157 views

What type of HMM-GMM I need

Context: I have 100 speech sentences that I asked my friend to speak. The vocabulary in the sentences are same but only the order of words are changed. My friend says that he spoke exactly what was ...
Pupil's user avatar
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How can you use HMMs and ANNs for on-line handwriting recognition?

On-line handwriting recognition is the task of converting a series of $(x(t),y(t))$ coordinates to symbols and words. In contrast to off-line handwriting recognition, where you only have a bitmap of ...
Martin Thoma's user avatar
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1 answer
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Solving the part-of-speech tagging problem with HMM

There is a famous part-of-speech tagging problem in Natural Language Processing. The popular solution is to use Hidden Markov Models. So that, given the sentence $x_1 \dots x_n$ we want to find the ...
user16168's user avatar
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3 votes
1 answer
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Viterbi algorithm recursive justification

I have a question regarding recursion in Viterbi algorithm. Define $\pi(k; u; v)$ which is the maximum probability for any sequence of length $k$, ending in the tag bigram $(u; v)$. The base case ...
user16168's user avatar
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4 votes
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Next-Word Prediction, Language Models, N-grams

I was looking into how a next-word prediction engine like swift key or XT9 can be implemented. Here's what I did. I read about n-grams here - en.wikipedia.org/wiki/N-gram and aicat.inf.ed.ac.uk/...
superuser's user avatar
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2 answers
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Hidden Markov Model in Tagging Problem

I try to understand the details regarding using Hidden Markov Model in Tagging Problem. The best concise description that I found is the Course notes by Michal Collins. The goal is to find a ...
user16168's user avatar
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4 votes
1 answer
2k views

Viterbi training vs. Baum-Welch algorithm

I'm trying to find the most probable path (i.e., sequence of states) on an hidden Markov model (HMM) using the Viterbi algorithm. However, I don't know the transition and emission matrices, which I ...
dx_mrt's user avatar
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Any very user friendly resources on the Baum-Welch algorithm?

I'd like to understand the Baum-Welch algorithm. I liked this video on the Forward-Backward algorithm so I'd like a similar one for Baum-Welch. I'm having trouble coming up with good resources for ...
Walrus the Cat's user avatar
3 votes
1 answer
182 views

Is it viable to use an HMM to evaluate how well a catalogue is used?

I was interested on evaluating a catalogue that students would be using to observe how is it being used probabilistically. The catalogue works by choosing cells in a temporal sequence, so for ...
Oeufcoque Penteano's user avatar