Questions tagged [probabilistic-graphical-model]

For questions about (probabilistic) graphical models (PGMs), which are probabilistic models where a graph represents the conditional dependencies between random variables. If the graph is a directed acyclic graph (DAG), the graphical model is often called a Bayesian network or Belief network. If the graph is undirected, it's often called a Markov network or Markov random field.

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What is the difference between Probabilistic Graphical models and Graph Neural networks?

While going over PGMs and GNNs, it seems like both leverage the graph data structure. The former has been used to represent causal associations (among other things), while the latter has a varied set ...
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Sampling deep directed graphical models without a markov chain

I was advised to split this post into separate questions! I was wondering why in the original GAN paper, it is mentioned that you can sample deep directed graphical models without a markov chain (well ...
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Question from Original GAN Paper: Variational Inference to Train Undirected Models

I was advised to split this post into separate questions. I am wondering why in the original GAN paper, it is claimed that variational inference is used in deep undirected models for inference. I was ...
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How accurate is this table from the original GAN paper summarizing difficulties and properties for deep generative models?

In the original GAN paper, they talk about how inference and training might be done in other deep generative models. In no particular order I was confused by: what is meant by "Learned ...
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In Probabilistic Graphical Model (written by Daphne Koller), what's the meaning of "parameter" in representation of the distribution?

I just started to read the PGM book written by Daphne Koller. In the chapter of Bayesian Network Representation(Chapter 3), there are some descriptions about the standard parameterization of the joint ...
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In RL as probabilistic inference, why do we take a probability to be $\exp(r(s_t, a_t))$?

In section 2 the paper Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review the author is discussing formulating the RL problem as a probabilistic graphical model. They ...
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How do I derive the gradient of the log-likelihood of an RBM?

In a Restricted Boltzmann Machine (RBM), the likelihood function is: $$p(\mathbf{v};\mathbf{\theta}) = \frac{1}{Z} \sum_{\mathbf{h}} e^{-E(\mathbf{v},\mathbf{h};\mathbf{\theta})}$$ Where $E$ is the ...
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Purpose of the hidden variables in a Restricted Boltzmann Machine

From the part titled Introducing Latent Variables under subsection 2.2 in this tutorial: Introducing Latent Variables. Suppose we want to model an $m$-dimensional unknown probability distribution $q$ ...
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How to make sense of label propagation formula in graph neural networks?

In the label propagation algorithm in section 3.2.3, we know the label of some nodes and we want to predict the label for the rest of the nodes whose labels we don't know. The update formula for this ...
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Deriving hyperparameter updates in Online Interactive Collaborative Filtering

I've been going through "Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms" by Wang et al. and am unable to understand how the update equations for the ...
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What is a conditional random field?

I new in machine learning, especially in Conditional Random Fields (CRF). I have read several articles and papers and in there is always associated with HMM and sequences classification. I don't ...
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How do probabilistic graphical models factor into modern machine learning?

I just finished the three-part series of Probabilistic Graphical Models courses from Stanford over on Coursera. I got into them because I realized there is a certain class of problem for which the ...
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389 views

What does a hybrid Bayesian network contain?

The field of artificial intelligence is so vast. There are many methodologies for handling continuous data, and I have just read about the hybrid Bayesian network. I just want to know that what a ...