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Questions tagged [markov-chain]

For questions about the use of Markov models in the field of AI/ML.

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How does this distribution change in "Understanding Diffusion Models: A Unified Perspective"?

In the paper Understanding Diffusion Models: A Unified Perspective, how did we go from equation $(44)$ to $(45)$? I couldn't find the details in the paper. How does the distribtuion for, the ...
Harry's user avatar
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Training Diffusion Probabilistic Models

My question is in regards to the training process for Diffusion Probabilistic Models by Sohl-Dickenstein et. al. and also Ho et. al. and this blog post For the derivation of the model log-likelihood ...
diffusionQuestion's user avatar
2 votes
4 answers

Forward Diffusion Process Derivation In Diffusion Models

In papers and other material regarding diffusion models the forward diffusion process is defined by adding a small amount of Gaussian noise to an image $x_0$ for $T$ time steps. In each time step the ...
nkam's user avatar
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In the original GAN paper, why is it mentioned that you can sample deep directed graphical models without a Markov chain?

In the original GAN paper (table 2), why is it mentioned that you can sample deep directed graphical models without a Markov chain (well, they say without difficulties, but others list MCMC as a ...
beelze-b's user avatar
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Looking for help on initializing continuous HMM model for word level ASR

I have been studying HMM implementation approaches on ASR for the last couple of weeks. This probabilistic model is very new to me. I am currently using a Python package called Pomegranate to ...
Zander's user avatar
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8 votes
1 answer

What is ergodicity in a Markov Decision Process (MDP)?

I have read about the concept of ergodicity on the safe RL paper by Moldovan (section 3.2) and the RL book by Sutton (chapter 10.3, 2nd paragraph). The first one says that "a belief over MDPs is ...
josealeixo.pc's user avatar
2 votes
0 answers

Can $Q$-learning or SARSA be thought of a Markov Chain?

I might just be overthinking a very simple question but nonetheless the following has been bugging me a lot. Given an MDP with non-trivial state and action sets, we can implement the SARSA algorithm ...
dezdichado's user avatar
6 votes
2 answers

What is the difference between a Bayesian Network and a Markov Chain?

I am trying to understand the difference between a Bayesian Network and a Markov Chain. When I search for this one the web, the unanimous solution seems to be that a Bayesian Network is directional (...
Newskooler's user avatar
3 votes
1 answer

How can I use a Hidden Markov Model to recognize images?

How could I use a 16x16 image as an input in a HMM? And at the same time how would I train it? Can I use backpropagation?
david david's user avatar
1 vote
1 answer

How is the probability transition matrix populated in the Markov process (chain) for a board game?

Following on from my other (answered) question: With regards to the Markov process (chain), if an environment is a board game and its states are the various position the game pieces may be in, how ...
mason7663's user avatar
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1 vote
1 answer

In the Markov chain, how are the directions to each successive state defined?

I'm watching the David Silver series on YT which has raised a couple of questions: In the Markov process (or chain), how are the directions to each successive state defined? For example, how are the ...
mason7663's user avatar
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2 votes
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Are there any ways to model markov chains from time series data?

I want to make a thing that produces a stochastic process from time series data. The time series data is recorded every hour over the year, which means 24-hour of patterns exist for 365 days. What I ...
JH Lee's user avatar
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Predicting Hot Categories In a Reference Manager

Reference managers like Zotero or Mendeley allow researchers to categorize papers into hierarchical categories called collections. The User navigates through a listing of these collections when filing ...
Max Suica's user avatar
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1 vote
1 answer

Can HMM, MRF, or CRF be used to classify the state of a single observation, not the entire observation sequence?

I learn that the Viterbi algorithm used for Hidden Markov Model (HMM) can classify a sequence of hidden states from the corresponding observations; Markov Random Field (MRF) and Conditional Random ...
drerD's user avatar
  • 298
3 votes
2 answers

Difference in continuing and episodic cases in Sutton and Barto - Introduction to RL, exercise 3.5

Excercise 3.5 The equastions in Section 3.1 are for the continuing case and need to be modified (very slightly) to apply to episodic tasks. Show that you know the modifications needed by giving ...
Jakub Bielan's user avatar
5 votes
1 answer

Detect patterns in sequences of actions

I have to analyse sequences of actions that look more or less like this JSON blob. The question I'm trying to answer is whether there are recurring (sub)patterns that different users adopt when asked ...
Morpheu5's user avatar
  • 101
0 votes
1 answer

Markov Model for a Traffic Intersection

I need some help in developing a Markov Model for a crossroads there is no one way road and i am assuming at this time that traffic is only allowed to go straight no turns are allowed. There are 4 ...
user avatar
3 votes
2 answers

Can an Markov decision process be dependent on the past?

As far as I know MDP are independent from the past. But the definition says that the same policy should always take the same action depending on the state. What if I define my state as the current "...
Miemels's user avatar
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7 votes
2 answers

What is a Markov chain and how can it be used in creating artificial intelligence?

I believe a Markov chain is a sequence of events where each subsequent event depends probabilistically on the current event. What are examples of the application of a Markov chain and can it be used ...
WilliamKF's user avatar
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