# Tagged Questions

The tag has no usage guidance.

12 views

### Bayesian Network Learning with Continuous/Mixed Data

I'm working to implement a Bayesian Network. I want to train a Bayesian Network from a dataset, but I am having a problem calculating Conditional Independence from datasets containing a mix of ...
47 views

### How to derive marginal probability in hybrid bayesian networks?

Suppose I am having the following hybrid network where $A$ is boolean and $D, E \& G$ are continuous random variables, Also suppose the following: ...
60 views

### how to calculate the evidence in a hybrid bayesian network

The wikipedia page on bayesian networks gives a clear example on bayesian network on discrete variables, its says that My question is how this will differ if S is continuous? Or more generally how ...
29 views

### How to compute the mean of a conditional linear gaussian distribution

In a bayes net context consider the following covariance matrix where G is the child node and D and E are continuous parents ...
52 views

### Use Linear Regression to Estimate Conditional Probability for Bayes Net?

When reading and watching video regarding building and using Bayes Nets, the examples typically use binary outcomes for the nodes. 'Probability of it raining', 'Probability of x disease', ect... ...
8 views

### How does catnet handle missing data

I am wondering how does catnet package handle missing data in Discrete Bayesian networks structural learning. As such, bnlearn and other packages bank on imputation for a complete dataset, but catnet ...
53 views

### Bayesian Network: Scoring functions for structure learning?

Which are the widely used scoring functions for structural learning? More, specifically I am interested in scoring function that favours the random variables which have binary possible states. For an ...
26 views

### How to compare the continuous outcome of two group of studies in a meta synthesis?

While doing a meta synthesis, I would like to state if there is a difference in the compost quality (e.g. the carbon content) between studies carried out in tropical and temperate region. Within each ...
34 views

### Statistical graphical models in practice

I've followed the probabilistic graphical models course in coursera and now I would like to apply what I've learnt with real data. I want to implement LDA on a large corpus of text and I was ...
47 views

### How to exploit relationships between independent variables?

Data: Each instance (representing a document) is a bag-of-entities (like BOW, except they're Wikipedia entities instead of words), so each feature is a binary or tfidf-like score based upon the ...
46 views

### Bayes Net Parameter Learning in pymc

My goal is to infer the conditional probability tables (CPT) from the classic rain, sprinker, wet grass problem. Normally in this problem we know the CPTs and, given an observation like "the grass is ...
56 views

### D-separation in a Bayesian Network [closed]

The above question asks to see if Radio is D-Separated from Petrol given certain evidence. For evidence (i), why would this mean D-Separation? If Battery is true, we have a inactive triple. If ...
17 views

51 views

### Bayesian networks from a table

Could someone help me with question 5.b. I understand that the probability of any of these occuring independently is 0.5 but how do I combine those into a joint distribution function? Is \$0.5 \cdot ...
88 views

### EM algorithm decreases!

I have used the Bayes Net Toolbox to build a small network, which consists of 3 nodes and is shown below. Node 1 is a Bernoulli random variable, node 2 is a Gaussian random variable and node 3 is a ...
278 views

### Bayesian Networks and discretization of variables using K-means clustering

In many approaches to learning Bayesian Networks a solution to tackle continuous variables is to discretize them and apply one of the well established techniques for learning Bayesian Networks ...
3k views

### Difference between Bayes network, neural network, Petri Nets and decision tree

What is the difference between Neural network, Bayesian network, Decision tree and Petri Nets eventhough they are all graphical models and visually depict cause-effect relationship. Thank you
68 views

### number of stochastic nodes in bayesian multivariate distribution?

I'm doing some bayesian modeling using BUGS - JAGS to be specific. I find it hard to infer how many stochastic (i.e. non-deterministic) nodes there really are when I use multivariate distributions. ...
877 views

### Gibbs sampling how to sample from the conditional probability? Bayesian model

I want to learn Gibbs sampling for a Bayesian model. How can I sample the variable from the conditional distribution? In this example, arrow means dependent; for example, ...
232 views

### Multivariate meta-analysis in R: how to investigate network of variables

I would like to conduct a meta-analysis to investigate the interaction of three variables:hair color (dark/light), gender (male/female) and size (continuous). I have three studies reporting effect ...
236 views

### Sufficient number of sample to learn Bayesian network?

I want to construct Bayesian network for a 800 genes(genes are my node/variables). I have only 30 cancer samples and 30 normal sample.so I want to create network for cancer samples and for the normal ...
213 views

### How to learn Bayesian Network Structure from the dataset?

I need to learn a Bayesian Network Structure from a dataset. I read the book titled "Learning Bayesian Networks" written Neapolitan and Richard but I have no clear idea. According to the book from ...
52 views

### Intuitive understanding of Local Probability Distribution

I'm learning Bayesian network. I have problem in intuitive understanding of Local Probability Distribution. Can anybody explain to me what it is?
242 views

### Do edges in directed acyclic graph represent causality?

I am studying Probabilistic Graphical Models, a book for self-study. Do edges in a directed acyclic graph (DAG) represent causal relations? What if I want to construct a Bayesian network, but I am ...
153 views

### I have a problem in bayesian networks get p(E|A)

I'm doing this book "Modeling and reasoning with Bayesian Networks" and I have this problem: ...
138 views

### Parameters and parameter estimation in graphical models

I try to understand parameter estimation and learning problems at Graphical Models, especially in directed ones (Bayesian Networks). But first of all, I try to understand what exactly a parameter ...
31 views

### Tried to overfit a Bayes net, but mean prediction error is worse than learned network?

I have variables A, B, C, D, and E. I am interested in building a classifier for A. I learned a Bayes net structure from the data using greedy search and BIC as a score. Call this network 1. Using ...
1k views

### Understanding d-separation theory in causal Bayesian networks

I am trying to understand the d-Separation logic in Causal Bayesian Networks. I know how the algorithm works, but I don't exactly understand why the "flow of information" works as stated in the ...