# Tagged Questions

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0answers
14 views

### Confusion about the concept of Bayesian Networks

I read a lot about Bayesian networks, including focused literature. However, what I have not yet understood properly is this: What is the use case of Bayesian networks that contain of more than 2 ...
1answer
64 views

### 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 ...
0answers
43 views

### Inference in Bayesian network Using bnlearn package

In this link Prediction of continuous variable using "bnlearn" package in R , the author talk about how I can find the conditionl probability of P(node(C)\ the rest node)=P(C\A,B,D,E,F,G) ...
1answer
31 views

### Bayesian posterior marginal probabilities

I feel like this should be obvious, but I don't see the approach. This question comes from the philosophy of science, so I will pose it first in that context and then in a probability context. In ...
0answers
12 views

### How to determine independence in a Bayes Network using joint distribution

So I've got a practice problem and I'm really confused by our prompt. We're given the typical "Burglar Alarm" scenario ( http://i.stack.imgur.com/QTXrZ.gif ) for explaining bayes network. We are told ...
0answers
17 views

### Value of X that maximizes P(Y|X) in a bayesian network

Given two discrete variables $Y$ and $X$ that belong to a bayesian network, and an assignment $y$ to $Y$, how can one find the assigment $x$ to $X$ that maximizes $P(Y=y|X=x)$? Is there a name to such ...
0answers
12 views

### Variational bayes precision on network with categorical hidden nodes and observed gaussian nodes

I have a bayessian network that has a basic DAG structure where each node of this basic DAG has categorical (Bernoulli - only two values, in fact) distribution and each of this categorical nodes has ...
0answers
31 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 ...
1answer
76 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: ...
1answer
89 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 ...
1answer
42 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 ...
1answer
147 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... ...
0answers
27 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 ...
1answer
116 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 ...
0answers
30 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 ...
0answers
51 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 ...
0answers
50 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 ...
0answers
78 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 ...
1answer
97 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 ...
0answers
22 views

### Need a statistic for comparing “strength” of Markov blankets in a Bayesian network

Working with Bayesian networks. I take a given network structure and fit its parameters on data. I am looking for a statistic based on those parameter estimates that allows me to compare Markov ...
1answer
71 views

### PyMC consistently under estimating results found in paper. Possibly not sampling enough?

I have been trying to build confidence in (my ability to correctly use) PyMC by working examples. Namely, I have been working on Chickering and Pearl 1997, and more specifically on their 'artificial' ...
1answer
43 views

### Algorithm for removing edge between Gaussian nodes in Bayesian Network

Given a densely connected Bayesian Network based on expert input, what is a good algorithm for looking for edges that could be removed? All the nodes are Gaussian. I could discretize the variables and ...
1answer
87 views

### Specify conditional probability of a continuous node given a continuous node as its parent

This question is essentially same as this one. The question is: How do you calculate conditional probability of a node in Bayesian network when it has a continuous node as a parent? However, I cannot ...
0answers
51 views

### Observed versus hidden variables for Bayesian network in this particular context

I am a novice in Bayesian networks. I have a problem which is best described (at least I think so) in the following story. One wants to predict earthquakes. Let's say it has 5 variables, the last one ...
0answers
52 views

### Learn the bayes net structure with latent variables while testing (but observed while training)

I want to use Bayesian network for data which has 5 types of variables which are inter-dependent on each other. Out of that, 1 variable is observed only while training but it is unavailable during ...
2answers
264 views

### Proving Bayesian Network must be acyclic

I am struggling to prove that Bayesian Network must be acyclic. Could anyone help me in proving this? I am trying to prove by constructing a cyclic graph and showing some contradiction of probability ...
0answers
40 views

### Flow of influence in a v-structure for Probabilistic Graphical Models

I'm not very sure I understand why an observed v-structure have different flow of influence behaviour for a directed and an undirected graph. What is the intuition behind the actual definition for ...
1answer
127 views

### Bayesian Network or Logistic regression?

The Bayesian Networks and Logistic regression can be used to predict events or give to each customer the propensity to have a behavior. Which are the advantages or disadvantages of these 2 methods? ...
1answer
42 views

### Computing mixture of Binomial distributions

I'm trying to model a simple Bayes net, with $n$ samples based on a (unobservable) Bernoulli parameter, representing a true state of the world. Let $T$ be a Bernoulli random variable, with ...
1answer
31 views

### Bayesian network learning can be derived from learning each conditional distribution separately?

Please helm I am wondering if we consider a learning the parameter of a bayesian network ,with a training set ,where each training set is a vector of values containing all the random variable ,in ...
1answer
72 views

### Does a Bayesian network include the CPTs?

I'm preparing slides for a lecture, and I require some guidance. I'm only talking about discrete variables. How would you formally define the concepts surrounding Bayesian networks? A Bayesian ...
1answer
38 views

### Bayes nets - calculating probabilities

Given a Bayesian network, say a -> b -> c, all binary random variables (I won't show the CPTs, assume they are given). You are told b and c are true. How do you calculate the P(a=True)?
0answers
252 views

### Which naive Bayes?

I am attempting to use a naÃ¯ve Bayes classifier in python (using scikit-learn), with two examples. The first example has 6 classes and 2 hypotheses, the 2nd example has 2 classes and 6 hypotheses. ...
1answer
113 views

### Bayesian models vs Bayesian network models

I'm new to statistical modeling and working on applications in spatial property prediction. Can you help me understand the difference between a hierarchical bayesian model and a bayesian network ...
1answer
168 views

### About the definition of bayesian network

In this PDF http://people.csail.mit.edu/yks/documents/classes/mlbook/pdf/chapter2.pdf page 5 says: Given a set of functions $f(x_i,pa(x_i))$ non-negative and sum to 1, we define a joint ...
1answer
68 views

### Markov blanket conditional distribution derivation

I am trying to derive the formula for the conditional distribution for a variable in a Bayesian network: $$p(x_j|x_{-j})=p(x_j|x_{pa(j)})\prod_{k\in ch(j)}p(x_k|x_{pa(k)})$$ I understand D-separation ...
1answer
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 ...
1answer
111 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 ...
1answer
367 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 ...
5answers
6k 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
0answers
86 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. ...
1answer
1k 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, ...
0answers
252 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 ...
1answer
250 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 ...
1answer
362 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 ...
0answers
56 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?
4answers
307 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 ...
1answer
200 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: ...
1answer
162 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 ...
0answers
36 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 ...