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0answers
47 views

How to implement a simple Bayesian Network for Time Series Data?

I'm a computer science grad student, with not much knowledge in Bayesian statistics, so I'm seeking for guidance for the simplest start. I have 10 variables, like demand, price etc. and I want to ...
0
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0answers
27 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
38 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 ...
0
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1answer
71 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 probability ...
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0answers
31 views

How to use Hartemink's discretization algorithm?

From the help documentation of the discretize function of the R package bnlearn: Hartemink's algorithm has been designed to ...
1
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1answer
26 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 ...
0
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1answer
45 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 ...
3
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1answer
55 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 ...
3
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1answer
133 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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0answers
80 views

Difference between fuzzy graphical network , Markov model and Bayesian network

Referring to this answer Difference between Bayesian network and neural network and causal inference, I have come across other graphical models (1) Fuzzy Cognitive Map and (2) Neuro-Fuzzy (3) Fuzzy ...
2
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1answer
518 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
38 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
35 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. ...
0
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1answer
164 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
179 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 ...
0
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1answer
76 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 ...
2
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0answers
72 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
47 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
161 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 ...
0
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1answer
92 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
72 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
106 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 ...
0
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0answers
102 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
24 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 ...
6
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2answers
391 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 ...
0
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1answer
156 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 ...
2
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0answers
227 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
34 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 ...
2
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0answers
133 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 ...
6
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2answers
132 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
39 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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2answers
232 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 ...
1
vote
1answer
161 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 ...
1
vote
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 ...
1
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1answer
190 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 ...
3
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1answer
75 views

why to do structure learning for Bayesian networks?

Given a very-large data set, if our goal is to do probabilistic inference, what are the main advantages of learning a Bayesian network from data and then, use the Bayesian network to compute ...
2
votes
1answer
197 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 ...
0
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1answer
75 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?
5
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1answer
595 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
65 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
226 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 ...
1
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0answers
30 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 ...
0
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1answer
29 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 ...
4
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1answer
60 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 ...
0
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1answer
73 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 ...
2
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0answers
77 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, ...
1
vote
2answers
205 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) ...
2
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1answer
152 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
69 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 ...
2
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0answers
79 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 ...