Questions tagged [bayesian-network]
A Bayesian network is a probabilistic directed acyclic graph. Nodes represent random variables in the Bayesian sense (observable or unobservable); edges represent conditional dependencies between nodes.
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Mutual Information of nonadjacent nodes in Bayesian Network
How do you compute the mutual information of two non-adjacent nodes in a Bayesian network?
In this case, what would $I(D;A)$ be? Would I need to take the conditional probabilities of all intemediate ...
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Bayesian network with partial info (4 nodes)
I have some (conditional) probabilities for a Bayesian network with binary variables, but not all.
My DAG is M->F->Y->C<-F and M->Y and M->C
I ...
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Dynamic Bayesian Network with Temporal Hidden State
Can someone direct me to papers or Python packages which I can use to develop a dynamic bayesian network with temporal hidden states. I found packages that can handle discrete hidden states. But that'...
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Time Series Prediction with Hidden Variables [closed]
I have time series data sampled every 10 seconds. I want to predict a target variable 𝑦 for 5 steps ahead using the input variable at the current time along with 10 past lags. My problem is that ...
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What is proposal distribution in Importance sampling
I want to learn importance sampling using a simple example. Consider the following example code which implement importance sampling using python for a simple Bayesian network.
I've read that we fix ...
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Independent statements in model definition and then DAG
In this paper in Section 3.1, they give a Baysian linear regression model and then a DAG, which I show below.
From my understanding a DAG tells us how the joint distribution can be factorised.
But in ...
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How to leverage the separable functions in MCMC sampling? [closed]
I'm considering the posterior of a parametric model via the Bayesian approach. More specificity, I have a parametric model $u(p_1,p_2, p_3) = u_1(p_1) \times u_2(p_2) \times u_3(p_3)$ and I want to ...
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Bayesian network extracting further conditional independence statements then just from d-separation theorem
Given a Bayesian network $(p,\mathcal{G})$, where $p$ is our joint distribution, and $\mathcal{G}$ is a DAG.
Then by the d-separation theorem we can deduce conditional independence statements, in ...
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BN node probabilities calculation
In the paper The use of Bayes and causal modelling in decision making, uncertainty and risk by Norman Fenton and Martin Neil
following BN is presented:
Quote from ...
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Do Bayesian networks have any rules with regards to zero probability RVs?
I am currently learning about Bayesian networks through Berkeley's AI course. In a Bayesian network, each node encodes the conditional probability of the random variable (RV) represented by the node ...
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Stable violation of faithfulness
Faithfulness is often justified by the argument that any violations of it require very specific "fine-tuned" parameters (for some appropriate SCMs/SEMs/SFMs), and that such violations are ...
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Use of composite dependent variable in Economics
I'm curious about research that employs a composite dependent variable in empirical studies. Specifically, I'm interested in analyses that utilize any composite indexing method while taking into ...
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Calculating Porbabilites in a Bayesian Network
I have got the following Network and porbabilites
And I need to Calculate the probability $P(C=False)$
I triend using the formula for joint porability distributions
$P(X_1,…,X_n)= \prod_{i=1}^{n}P(...
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Definition of $\text{do}$ operator [closed]
I'm looking for a hint in understanding semantics of $\text{do}$ operator. Starting from the original distribution $P$, an intervention $\text{do}(X=x)$ takes us to another distribution $P_x$ - in ...
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On showing indepdedence of a collider in graphical model
In the following slide: it seems how it got A and B are independent is rather circular. The reason is it assumes $p(a,b,c) = p(a)p(b)p(c|a,b)$ and then marginalizes over $c$ to get $p(a,b) = p(a)p(b)$,...
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