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14 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) ...
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1answer
27 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 ...
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0answers
11 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 ...
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0answers
16 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 ...
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0answers
9 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 ...
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0answers
23 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 ...
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1answer
68 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: ...
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1answer
80 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 ...
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1answer
39 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 ...
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1answer
84 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... ...
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0answers
22 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 ...
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1answer
88 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 ...
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0answers
28 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 ...
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0answers
45 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 ...
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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 ...
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0answers
62 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 ...
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1answer
81 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 ...
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0answers
20 views

Multivariate distributions, k-margin's, Bayesian Networks, and dependency

Suppose that we have a multivariate probability distribution with four variables, X, Y, Z, and W. Let us assume that the joint distribution can be factored as follows: $$ f_{X,Y,Z,W}(x,y,z,w) = ...
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0answers
20 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 ...
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1answer
66 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' ...
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1answer
38 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 ...
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1answer
81 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 ...
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0answers
50 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 ...
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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 ...
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2answers
222 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 ...
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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 ...
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1answer
106 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? ...
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1answer
37 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 ...
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1answer
28 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 ...
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1answer
69 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 ...
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1answer
37 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)?
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218 views

Which naive Bayes?

I am attempting to use a naïve 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. ...
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1answer
99 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 ...
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1answer
154 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 ...
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1answer
58 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 ...
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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 ...
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1answer
100 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 ...
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1answer
336 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 ...
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5answers
4k 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
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0answers
76 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. ...
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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, ...
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0answers
245 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 ...
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1answer
242 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 ...
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1answer
315 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 ...
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0answers
54 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?
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4answers
280 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 ...
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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: ...
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1answer
154 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 ...
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0answers
35 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 ...
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2answers
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 ...