# Tagged Questions

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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### What is the junction tree of the following graph?

I got so confused in the remaining steps (from triangulated graph to junction tree) after turning my Bayesian network to a triangulated graph. Would anyone please help?
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### Bayesian Dirichlet equivalent (BDe), Bayesian Dirichlet equivalent uniform (BDeu) and Mutual Information Test (MIT)

To estimate structures of Bayesian networks, I am thinking about three score functions, BDe, BDeu and MIT. I have several questions. What are the differences between BDe and BDeu? Can I convert BDe ...
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### Why computing P(x,D) is simpler than P(x|D) in exponential bayesian networks?

I am reading this tutorial on variational inference and wonder why the statement in the question title which is mentioned on page 3 is true.
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### causal correlations for binary variables

Let's assume we have binary vectors where we want to find correlations and the possible causal relations between the variables in R. 1- does "Bayesian Network structure" give the correlated variables?...
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### Conditional probability table and Bayesian Network strength (R bnlearn package)

I'm using bnlearn package in R, what is the interpretation of the conditional probability tables ? as an example the output for the following code: ...
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### In Bayesian networks does hard evidence make P(evidence) = 1?

I've been attempting to understand how Bayesian networks work when evidence is applied to them, and in the book I'm currently reading, there are what appear to be contradictory statements, and I don't ...
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### how to use a Bayes Network Classifier weka model in our application

we are working on a Persian news summarizer algorithm. we used Weka API and BayesNet classifier to build a model for predicting sentences to be include in summarized text. the output result of Bayes ...
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### showing causality between police killings and demographics (i.e. race, class, gender, location)

I've got a whole lot of police data and was wondering what sort of approaches I could use to show strong correlation, and if possible, causal effects, between police killings/arrest & call-ins for ...
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### Bayesian networks and weird probabilities

I have to solve the following problem: Suppose we have a bayesian net in which we have the following variables: R, PA and PR Let: P(R) = 0.1, P(PA) = 0.5, P(PR|R, PA) = 0.6, P(PR|¬R, PA) = 0.4, P(...
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### Causal Graph using Bayesian Network

I am currently doing a project in which the dataset is a lung cancer dataset. There is a training file which consists of 7 unnamed parameters (Attributes) and each of them have around 1000 values ...
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### Modelling a Static Bayes Net versus Dynamic Bayes Net

I have a Bayes Net with 20 variables, but I found out that one of the Parent variables is dependent on the previous value of its Child as: C(t-1)->P(t)->C(t) C and P are binary (True or False). All ...
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### How does loop belief propagation differ from variational message passing?

I'm reading Yedidia et al.'s paper and Winn et al.'s paper. The two approaches (LBP and VMP) are pretty similar to each other: Eq (5,7) in Yedidia are similar to Eq (19,20) in Winn. They both update ...
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### Using likelihood for event correlation in Bayesian network

Assume we have a simple Bayesian network, each event (alarm propagation) is a binary vector. Each node can trigger an alarm independently, but alarm propagates in the network with the probabilities of ...
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### Estimating P(C|A,B) from P(C|A) and P(C|B): Bayes Rule? Bayes Net? Classifier?

Doing some ecommerce analytics, I want to understand click propensity broken out by different features present in users' profiles. In this scenario, it's easy to test click propensity $p(C)$ broken ...
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### How to deal with the factors when moralize a directed network?

Consider we have a simple V-Structure Bayesian network which we use it for model some random variables. in other words we have a distribution $P(C|A,B)$ where A and B are the parents of C in the ...
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### Bayesian Network produces different directions depending on order

I'm trying to fit a Bayesian Network model with bnlearn to determine the direction that users go from different actions (i.e.: do seeds lead to joins, or joins to ...
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### Combining one class classifiers to do multi-class classification

I am working on a 3-class classification problem. The classifier I'm using is Bayesian Networks which provides me with a classification accuracy of around 60%. When I do a two-class classification, I ...
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### Got an entropy-ish function for a multinomial distribution? Graph theory and Bayes net related

I have a discrete variable $X$ that can take on one of three states; $a$, $b$, and $c$. Thus it has two parameters $p_a = P(X = a)$ and $p_b = P(X = b)$, of course $P(X = c) = 1 - p_a - p_b$. I am ...
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### How to apply a fitted Tree-augmented Naive Bayes classifier to new cases

I am running Tree Augmented Naive Bayes algorithm in R and I have got the desired network. However, unlike logistic regression, Bayesian is non-parametric i.e. I do not have any coefficients which I ...
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### Can optimising thresholds for discretisation lead to overfitting in Bayesian networks?

In Bayesian networks continuous data is often made discrete, for example: < 21.5 becomes 0 21.5 > .. < 43 becomes 1 > 43 becomes 2 If you run an ...
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### Discretization for a bayes network model with small sample

I have been playing around with a Bayesian network toolbox for prediction and classification. I have had good success with the examples but I'm now stuck on how I should proceed with my scenario. I ...
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### Improving the results coming from an image recognition API

We are developing a software application that will automatically suggest tags (keywords) for images that are being uploaded into a database of already-tagged (by a human) images. We are using a 3rd ...