# Questions tagged [bayesian-network]

For questions related to Bayesian networks, the generic example of a directed probabilistic graphical model. Includes dynamic Bayesian networks, e.g. Hidden Markov Models (HMMs) and Kalman Filters. For applications of Bayesian networks in any field, e.g. machine learning. NOT for general questions about Bayes' theorem, Bayesian statistics, conditional probabilities, networks, or graph theory.

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### Bayes Net. How are the values P(G | D,I) calculated?

Example Bayes Net I have been looking into Bayesian Networks but I keep getting hung up on a simple dependence in most example problems which is how are the values in the conditional probability ...
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### Influence of conditioning a node in an undirected graph on other nodes

Assume that I have a $D$-variate random variable $\mathbf{X}$, and a $D$-by-$D$ precision matrix denoting the strength of an undirected graph's edges between each of its $D$ univariate nodes (where ...
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### Prior in variational autoencoders

I am currently dealing with variational autoencoders where I've read the original paper "An introduction to variational Bayes" from Kingma and Welling. I am currently still a little confused ...
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### chain rule ordering

suppose I want to compute the joint probability $P(X,Y,Z)$ with chain rule. Is it true that there will be $3!$ possible factorization ? or is there more ? I got 6 for this example: $P(X|YZ)P(Y|Z)P(Z)$...
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### Distributed Hypothesis Testing--reference question

If you don't know the correct keyword, you can still miss a key literature search: I have a problem in distributed Bayesian detection with a serial (or tandem) network topology. The probability ...
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### Optimal proposal distribution for Bayesian networks

I was going through chapter 12 of Probabilistic Graphical models by Koller and Friedman. The chapter is on Particle-Based Approximate Inference On page 505, where unnormalised importance sampling is ...
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### calculate probability using variable elimination

Consider the following Conditional probability for the Bayesian Network: By using variable elimination, how to calculate the following probability? I am summing all the terms related to $E$, then ...
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### Expected Value of Determinant of Wishart Random Variable

In the paper, "Robust Bayesian Clustering" by Cedric Archambeau and Michel Verleysen, the authors have developed a variational Student-T mixture model that is unique because it assumes ...
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### Algorithm for computing joint probability distribution from conditional probability table using tensor multiplication in Bayesian Network?

I get stuck on this problem. If in a Bayesian network, how can we do tensor multiplication on the conditional probability table so that it eventually gives the joint probability distribution? If a ...
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### How to calculate an intersection in bayesian network

I was trying to solve this question, but i don't know how to proceed from there. And i am not sure how to compute $P(A|X_1,X_2,\neg X_3)$ or $P(A \cap X_1\cap X_2)$. It seems like i don't understand ...
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### Bayes Rule combining two sensors

You are programming a demining robot. As the robot drives along, the prior probability of a mine being in its immediate vicinity is 0.001 The robot is equipped with a mine detecting sensor which ...
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### Why does the conditional independent rule of INTERSECTION require STRICT POSITIVE DISTRIBUTION?

Recently, I was confused with the proofs of some conditional independent rules (decomposition, weak union, contraction, intersection), particularly the conditional independent rule of INTERSECTION. In ...
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### Minimal Bayesian network for a given subset of variables?

Let $G=(V, E)$ be a DAG. Let $\mathrm{dom}$ be a domain for each node in $V$ and $P$ be a joint probabiliy distribution over those domains, that factors as a product of conditional probability ...
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### Equivalence of two Bayesian Network Structures

Consider two Bayesian networks with binary random variables, whose directed acyclic graphs are shown in the following figure Define $p_G(A,B,C)$ and $q_{G'}(A,B,C,D,E)$ as the joint probability ...
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### Does increasing the number of edges in a Bayesian network improve its perfomance?

Let's assume we have a bayesian net with N number of nodes and M number of edges. If we're somehow able to increase the number of edges while maintaining the same number of nodes will our bayesian net ...
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### How do I get $P(A|C,!B)$ with the following probability distributions?

I have the following probability distributions table: I know that $P(B)=0.6$ and that: A may or may not have B Some A have C How do I get $P(A|C,!B)$? I built the following baysiean network: I ...
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### How do I find the P(B | D = T) in this bayesina netowrk?

How to find P(B | D = T) in the following Bayesian network?
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### Shortest path with jumps (dynamic Bayesian network)?

Suppose I have the following graph structure: It has the following properties: There are four states $\mathcal{S} = {q,s_1,s_2,s_3}$ where $q$ is some origin state where we start from (though it is ...
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### How do I find gibbs sampling equations for finding circularly-symmetric Gaussian shaped objects

I have an image D that have some gaussian noise and circularly-symmetric Gaussian shaped object which is defined by $$f(\boldsymbol{x};\boldsymbol{a}) =Ae^{-\frac{((x-X)^2+(y-Y)^2)}{2R^2}}$$ where a={...
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### Directed edges in Bayes net could have no effect?

After taking a risk analysis course, I am getting myself familiar with Bayes nets. Currently, I am looking at a common example of whether to take an umbrella on a walk. This is in the context of ...
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### True Loss for Bayes Classifier with Two Classes

For a Bayes classifier of two classes (say 0 and 1), I'm not understanding how the largest possible true risk would be 0.5? I'm assuming that we assign a 0 loss for a correct classification and a loss ...
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### Simple Bayes Net Calculation

I'm reading a paper and I'm trying to understand the Bayes net example they give. Here's the Bayes network in question: Here's the simple calculation they perform using the net above: How do they ...
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### Using Bayesian Networks to solve this localization problem?

I'm reading this paper, which I'll summarize here: Let a sensor network (in this case, a network of radio receivers) consist of $N$ sensor nodes at locations $S = \{ S_1 \cdots S_N\}$. Let $S_i^x$ ...
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### Is my Bayes belief network theory correct?

I am currently trying to learn Bayes' theorem, and in turn, Bayesian belief networks. I haven't done any 'real' maths in nearly 20 years, so I am rusty to say the least. I am trying to determine the ...
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