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13 views

Why are Bayesian classifiers “robust to noise”?

In many different settings I've read that Bayesian classifiers like Naïve Bayes and Bayesian Networks are more robust to noise in the input data than other classifiers. I'm wondering what the evidence ...
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0answers
13 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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34 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
137 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
59 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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0answers
47 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
44 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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2answers
83 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
84 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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0answers
69 views

References for spatial modeling with Bayesian belief networks in medical applications

I want to do research in spatial data mining where I want the concept of Bayesian belief networks applied on a medical domain like, for example, cancer. I have been searching for recent papers in ...
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1answer
92 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
83 views

Bayesian Networks: Does the d-Separation Property originate from the basic Markov Property?

I asked the following question in order to gain some intuitive understanding about the d-Separation property in Bayesian Networks a while ago: Understanding d-separation theory in causal Bayesian ...
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0answers
18 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
189 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 ...
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1answer
124 views

Why do Bayesian Networks use acyclicity assumption?

Actually, this question is more or less a duplicate of the one which I have asked on math.stackexchange two days ago. I did not get any answer there but I think now here is a better place to ask ...
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0answers
39 views

Network/structure learning

Given a data set $\mathbf{X}\in\mathbb{R}^{n\times p}$, where $n$ is the number of samples (observations) and $p$ is the number of features, I would like to know what kind of methods exist for ...
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0answers
185 views

What is a recommended Python framework for Bayes Nets [closed]

What is a good Python framework to use for statistical analysis using Bayes Nets. The following statistical frameworks in one way or other do not support expected features. Python Scikit does not ...
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0answers
23 views

How can I update a Bayesian network model given new data on only a subset of the variables in the original model?

There are several methods for inferring network structure in Bayesian networks, given data. In my case I have a Bayesian network model built from old data, and I have a new source of data that I want ...
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0answers
104 views

How to generalize Particle Filters (w.r.t. multiple states)

I'm using particle filters for inference in a hidden markov model with an infinite state-space. My current state-variable is multidimensional and there are interdependencies between some dimensions. I ...
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2answers
126 views

Regarding the formula of using $\text{P}(Y|X)$ to compute $\text{E}[X]$

When reading a presentation on "expectation propagation," I found a strange formula for computing $\text{E}[X]$ from a conditional probability: $$\text{E}[X] = \frac{\int x P(y_i|x) dx}{\int P(y_i|x) ...
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0answers
26 views

Bayesian Belief Network - directions of arcs between nodes

I generated a BBN below based on environmental variables and a response of some organism. My aim here is to see how environmental variables (A-H in a graph above) interact with each other and ...
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1answer
107 views

How to measure the distance between two Bayesian networks?

Given a set of random variables $\{X_1, X_2, \dots, X_M \}$ and a (complete) dataset $D$, I have used some standard (greedy) algorithms to find good candidates to be the "true" bayesian network ...
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1answer
104 views

What are the structural rules for arc reversal in a Bayesian network?

Given a Bayesian network, if I reverse the edge from $X \rightarrow Y$, what additional edges do I need to add to the structure of the network? I know that there are some rules about linking (adding ...
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1answer
48 views

Simple Bayes network

Given the following Bayes network: with $p(k=t)=.2$ $p(o=t)=.1$ $p(s=t|k=f,o=f)=.0$ $p(s=t|k=f,o=t)=.2$ $p(s=t|k=t,o=f)=.5$ $p(s=t|k=t,o=t)=.95$ how would I calculate $p(s=t|o=t)$ and ...
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1answer
122 views

Simple Bayesian network

I have three variables: GENDER {“Male”, “Female”} AGE {“0-18”, “19-30”, “30-60”, “60+”} REGION {“Europe”, “Asia”, “Africa”, “America”} From the literature I “know” that: Males who live in Asia ...
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0answers
42 views

why to do structure learning for bayesian networks?

Given a very-large dataset, if our goal is to do probabilistic inference, what are the main advantages of learning a bayesian network from data and then, use the BN to compute conditional ...
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1answer
126 views

Generating Bayesian network graph with dsc file

I'm using a free version of Bayesian network software called Netica. It allows only 15 nodes for the free version. Do you know any other software or R package that generate a kind of graph below using ...
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1answer
66 views

Finding the corresponding bayesian network of a predefined joint probability distribution

Given a joint probability distribution over the variables $X_1,X_2,\dots,X_n$. Is there an algorithm for constructing the corresponding Bayesian Network?
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1answer
282 views

Bayesian network inference using pymc (Beginner's confusion)

I am currently taking the PGM course by Daphne Koller on Coursera. In that, we generally model a Bayesian Network as a cause and effect directed graph of the variables which are part of the observed ...
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0answers
47 views

Bayesian belief network for finding combinatory effects of multiple environmental variables on allele frequency

I'm a beginner to Bayesian belief network (BBN). I read a few articles on introduction to BBN. So I know a general idea of BBN. But I'm struggling to construct a graphical network and conditional ...
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0answers
133 views

Step by step example of calculation of posterior probabilities for Bayesian network?

I am learning Bayesian networks. I am getting difficulties in understanding computation part of posterior probabilities. If there is any ready made step by step example then it would be really ...
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0answers
176 views

Causal models in pymc3

I'm trying to fit a causal model. Participants in a task are trained on the model then asked for their belief in all the joints over the variables (e.g., what are the chances of observing an item with ...
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0answers
24 views

Evidential reasoning in Gaussian Bayesian Networks

I am working on Gaussian Bayesian Networks (GBN) i.e. the Bayesian Networks where all the random variables are continuous in nature. I am seriously trapped in the problem of evidential reasoning in ...
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1answer
28 views

How was this summation performed?

Say I have the following simple Bayesian network involving 3 r.v.s A, B, and C: $$ A \rightarrow C \rightarrow B $$ I am trying to prove that A and B are conditionally independent given by ...
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1answer
57 views

Latent variables in Bayes nets with no physical interpretation

In Pattern Recognition and Machine Learning Bishop writes about Bayes networks: For practical applications of probabilistic models, it will typically be the highernumbered variables ...
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1answer
70 views

Are probability graphic models useful for predictive modelling?

Are Probability Graphic Models (say specifically Bayesian Networks) useful for predictive modelling in terms of large data (100,000 - 1,000,000 rows) and many variables (hundreds)? Meaning, is this ...
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0answers
70 views

Static Bayesian networks using p-values

In your opinion, what is the best way of handling Bayesian networks using continuous data, in this particular case, p-values? I have read about several discretization techniques, Gaussian approaches, ...
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2answers
165 views

Moralization and triangulation on belief networks

Assume that I have a belief network with a set of nodes. In order to create a valid junction tree I have to moralize the graph. Assume now that I have nodes with more than 2 parents (e.g 3 parents) ...
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1answer
120 views

Learn a joint distribution from incomplete samples

Suppose I want to learn a joint distribution $p(x_1, \ldots, x_n)$ and have a collection of samples $x^k_1, \ldots, x^k_n$ for each $k$. Assume some values $x^k_i$ are unknown, so the samples are ...
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0answers
66 views

Latent groups in Bayes net with BUGS

I'm modeling a Bayes net with OpenBUGS, and I find problems to specify some of the parameters and their priors. The aim of the model is to identify latent groups in the data from a sample of human ...
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0answers
60 views

Using Bayesian Networks to Understand Expected Revenue

I'm trying to understand Bayesian Networks and am attempting to apply it to solve some problems in the world of marketing, most notably search engine marketing. I have a data on EACH in click to a ...
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0answers
52 views

Normalization for dynamic Bayesian network inference?

I would like to know whether preprocessing of the dataset is required for dynamic Bayesian network inference or not?
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1answer
120 views

Odd results from Bayesian network in R

Related to question here. I've been trying to teach myself about Network Analysis, and developing DAG charts in R. Let's say that I have the following data. ...
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1answer
926 views

Prediction with Bayesian networks in R

I've been trying to teach myself about Network Analysis, and I've been able to develop DAG charts in R. However, I've looked through three or four R packages and have seen little in the way to a ...
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1answer
1k views

How to write a poker player using Bayes networks

This is my first question on stackexchange and also my first time implementing a Bayesian network so I will apologize ahead of time for any novice mistakes I make. The goal of my project is to ...
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0answers
123 views

Observation (evidence) in dynamic Bayesian networks

I am wondering how the probabilities of the observation nodes in dynamic Bayesian networks are set. I want to know whether the probabilities are monitored or are given by sensors? So, what does the ...
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0answers
41 views

Empirical change of DAG for Bayesian Network

While working on some problem in bioinformatics I applied bayesian network algorithm for classification purposes. As predictors I took a window of sequence of aminoacids and for dependent variable I ...
4
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2answers
405 views

Bayes Network/Conditional Probability Visualization Tools

I'm hoping that someone could suggest a tool for viewing conditional probabilities withi. I am currently using Weka, but the ability to view the conditional probability tables of nodes within the ...
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0answers
48 views

Updating beliefs networks when new event occur in the future

I'm beginner with the Bayesian networks and I want to know how the beliefs networks update the posterior probabilities when new event occurs in the future. I want to test an example, I am starting ...
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2answers
101 views

Updating the probability distribution when removing links from a Bayesian network

I have spent a fair amount of time trying to solve this problem but I can't find the solution. More specifically, I have the following matrix: ...