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Questions tagged [bayesian-network]

A Bayesian network is a probabilistic directed acyclic graph. Nodes represent random variables in the Bayesian sense (observable or unobservable); edges represent conditional dependencies between nodes.

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Is this task about soft evidence or just creating of Bayesian Network?

I have a task. We have two sensors that are meant to detect extreme temperature, which occurs 20% of the time. The sensors have identical specifications with a false positive rate of 1% and a false ...
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12 views

Is this task about soft evidence or just creating of Bayesian Network?

I have a task. We have two sensors that are meant to detect extreme temperature, which occurs 20% of the time. The sensors have identical specifications with a false positive rate of 1% and a false ...
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1answer
39 views

Understanding the parameters needed for a distribution in Bayes networks?

Since I have a discriminative mindset hardly can I intuit the so-called parameters needed to specify a distribution in a generative Bayesian Network. I'd like to borrow an example from this blog. If ...
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10 views

Meaing for MOAC in Spherical Payoff

I want to implement this metric Spherical Payoff mentioned in both articles and Netica software to validate my bayesian network (through a test dataset), here are the formula that I got from my ...
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0answers
5 views

In which applications bayesian network are more promising to capture the uncertainities and understanding?

I know, there are lots of question on difference between NN and bayesian network, What i am specifically talking about modelling and uncertainty capturing? As we know, bayesian ML methods are very ...
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9 views

Initializing structural expectation maximization for learning Bayes net structures

I am using bnlearn in R to learn Bayesian network structures. It has a structural.em method for learning with missing data that ...
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0answers
14 views

How to validate the results of bayesian causal network?

There are many ways of validating predicting the results: MSE, MAE, AIC, CV, etc.. But I do not hear any validation way of causality. If the true networks not available, how to make sure the results ...
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12 views

Algorithms for combining Bayesian networks? [closed]

Are there any algorithms for combining multiple Bayesian networks? For example, let's say I have 5 variables A, B, C, D and E, and I build 5 Bayesian networks on different random subsets of these, let'...
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1answer
27 views

Order of Conditional Independence Tests

I'm studying the PC algorithm for learning the structure of a Bayesian Network. One of the steps refers to performing several rounds of conditional independence tests of increasing order, zero, first,...
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1answer
59 views

Which book has the right conditional independence formula? [closed]

I'm getting crazy. I've just started to learn probability and, after it, Bayesian networks. I don't know so much about probability, that is why I'm getting crazy. I'm using this book to study a ...
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9 views

Turning dichotomies into a typology

Creating a typology and typing an object is hard. What isn't hard, however, is creating dichotomies and seeing which dichotomies an object satisfies. Here is my case study. Myers-Briggs typology for ...
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12 views

number of parameters in Bayes Net

This might be a bit rudimentary. Have some problems in understanding the number of parameters in Bayes Net How do we calculate the number of parameters in the Bayes net in the preceding diagram? ...
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1answer
53 views

How to know if a conditional independence is true or false [closed]

I'm learning Bayesian networks and I have to "guess" if the following conditional independence are true or false using the following table: And the conditional independence are: $I_p(A, B)$ $I_p(A, ...
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1answer
23 views

How to use a G-Wishart distribution in stan

I would like to use the following kind of prior in a Stan simulation $$ f_{K \mid G} (k \mid g) = \frac{1}{I_g(b,D)}|k|^{\frac{b-2}{2}} \exp \biggl \{ -\frac{1}{2} \text{tr} (Dk) \biggr \}\mathbb{1}_{...
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0answers
86 views

EM Algorithm for Bayesian Networks with missing data

Setting: learning parameters of Bayesian Network (BN) with missing data. Algorithm: Expectation-Maximization. Question: suppose I am in the M-step, and that in the complete data there are no ...
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0answers
7 views

Test for the “significance” of Bayesian network edges

I have a Bayesian network, and there is an edge between variables $A$ and $B$. A -> B I can calculate BIC score of the whole network, and based on that, I can ...
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1answer
18 views

Relation between a Gamma prior and posterior in terms of paramters

I am doing a maths exercise and I have found out that the prior of my parameter is is a inv.gamma (alpha, beta), the likelihood is an exponential distribution. Finally I have discovered that my ...
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0answers
19 views

How are bayesian networks created from an attribute matrix and target vector?

I'm very familiar with correlation networks but I can't seem to grasp my head around how Bayesian Networks are constructed. How are the "edges" determined? How is the structure determined? I was ...
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29 views

Building independence maps (I-maps) from data

I am just getting into Bayesian networks, and I am having a hard time understanding how this algorithm works: http://pgm.stanford.edu/Algs/page-79.pdf (The algorithm is from Probabilistic Graphical ...
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11 views

Bayesian network (learn_struct_mwst)

I want to modeling a network with bayesian network. for finding the order i want to use learn_struct_mwst from nbt toolbox in matlab. can anybody help me to use this function? The code that i write ...
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1answer
29 views

Can't understand D seperation [duplicate]

I have the following network: I am told that B is independant of D. Why is this the case? Shouldnt that they are both connected to C break that independence based on the V shape? In this case, I am ...
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0answers
42 views

Bayesian Networks - Factor Graphs - Belief Propagation - Numerical stability

I am trying to do inference for a Bayesian Network with discrete probabilities. I converted the network to a factor graph and implemented the sum-product algorithm (belief propagation). My goal is ...
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158 views

Step-by-step tutorials on Bayesian Networks

I'm trying to study Bayesian Networks (BN), but during classes [1] we covered just some theory and no exercises were given. Therefore I'm looking for step-by-step tutorials or solved exercises for BN ...
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25 views

Discrete latent variables in Bayesian Network

I am creating a Bayesian Network where all nodes are discrete. Using the available data, I have learned the structure of the network using the Hill-Climb algorithm (...
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0answers
33 views

Accuracy Measure for Bayesian Network

I was trying with INSURANCE data in bnlearn. For measuring the model accuracy, I tried with different nodes for prediction. With each node, the prediction shows different accuracy rate. So, my doubt ...
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12 views

Factor Graphs and Sum-Product Algorithm

I am trying to study Factor Graphs and the Sum-Product Algorithm. I'm having a hard time understanding what are these factors. For instance, when I see something like $$ f(x_1, x_2, x_3, x_4, x_5) = ...
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0answers
126 views

Bayesian networks with continuous variables in Python [closed]

I am trying to create a Bayesian network model (Probabilistic graphical model) in Python, that can handle continuous data. I have tried using pgmpy, but the 'fit' function in pgmpy has not yet been ...
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1answer
62 views

Can causal Bayes Nets compute counterfactuals? If so, are they “worse” than structural causal model counterfactuals?

I was under the impression that you couldn't perform counterfactual reasoning on causal Bayes nets using inference algorithms and do-calculus, but I realized recently that this might not be true. ...
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10 views

How to specify initial probability values for variables in a dynamic Bayesian network using Bayes server

I am trying to create a dynamic Bayesian network for parameter learning using the Bayes server in C# in my Unity game. The implementation is based on this article. A brief explanation of the model ...
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1answer
30 views

What is the connection between Bayesian Networks and the models done in Probabilistic Programming?

For what I’ve read about them, they are very similar. Both model probabilistic models in which the nodes are random variables and the edges are dependencies among them. However, the literature I’ve ...
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1answer
45 views

How to choose an appropriate variational distribution?

I work in deep learning research and I am trying to learn how to use variational inference in order to approximate a posterior over the learned weights. I have looked extensively at Yarin Gal's ...
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1answer
20 views

Using the Expected value of the log as a score for the anomaly detection instead of just the expected value

While dealing with anomaly detection using a probabilistic model I need to compute the probability of an example coming out of the model I built. More specifically: If $p(X)$ is the model I built and ...
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2answers
43 views

Prove dependency of variables in a Bayes net (CS188)

I'm trying to understand how the conclusion of $P(x|z)=1, \forall x = z$ is reached. I can understand it intuitively but I'm having a big trouble figuring out how to really 'chug the math'. I've been ...
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0answers
40 views

How to find the node with the largest influence in a Bayesian network?

I have a Bayesian network with the following structure: Each node has two states, true (T) and false (F). Evidence can be observed for the nodes at the top and there's a node for the final result at ...
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1answer
146 views

Bayesian networks for one-class classification

From the definition of one-class classification in wikipedia: In machine learning, one-class classification, also known as unary classification or class-modelling, tries to identify objects of a ...
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0answers
20 views

Suitable Machine Learning Classifier for Numerical and categorical dataset?

Does anybody know! what are the suitable machine learning algorithms --e.g., bayesian network, decision tree, OneR, etc.-- to learn the model from a dataset with limited instances --e.g, less than 10 ...
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0answers
102 views

Metropolis sampling for Bayesian networks

Gibbs sampling is a profound and popular technique for creating samples of Bayesian networks (BNs). Metropolis sampling is another popular technique, though - in my opinion - a less accessible method. ...
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56 views

HMM - Approximate log likelihood using Gibbs sampling

I am studying MCMC approaches to HMMs and Factorial HMMs. I am reading this paper 'introduction to hidden markov models and bayesian networks': http://mlg.eng.cam.ac.uk/zoubin/papers/ijprai.pdf In ...
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53 views

Can someone explain why Bayesian networks are called “Bayesian”

I have been reading Jensen's book on Bayesian Networks and Decision Graphs as well as the Deep Learning book by Bengio, et. al. I am trying to understand why undirected graphs are referred to as ...
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1answer
321 views

Threshold to build confusion matrix?

I a have data set with 10 sections of data and each section shows one day observation. I designed the training and test set as follows: 8 sections for training the data and the last two sections for ...
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0answers
8 views

Making use of time intervals in computing conditional probabilities in sequential data with time

I have sequential data X and a binary target y (which is the last event in the sequence). Each row represents a unique sequence of events with a label, 0 or 1. Potentially, I need to estimate a ...
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0answers
29 views

Hybrid Bayesian Network - Conjugute Prior for Bernoulli Variable with Discrete and Continuous Parents

I have a discrete variable $x_a$ for which the likelihood function is Bernoulli. Within the Hybrid Bayesian Network, $Parents(x_a)$ includes both continuous and discrete variables. Is there a ...
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0answers
30 views

Dilemma concerning prediction of user satisfaction

Imagine that you have data with information about users : Age, sex, location, etc ... with the answer to two additional questions : Q1 = Have you tried product P ? yes/no ; Q2 = Did you enjoy product ...
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1answer
94 views

Dependency of non descendant in Bayesian Network

I am learning Probabilistic Graphical Models from a book by Daphne Koller and the basic definition of a Bayesian Network says this: I tried to make a counter example to this and am confused if the ...
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3answers
3k views

Does statistical independence mean lack of causation?

Two random variables A and B are statistically independent. That means that in the DAG of the process: $(A {\perp\!\!\!\perp} B)$ and of course $P(A|B)=P(A)$. But does that also mean that there's no ...
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1answer
51 views

Understanding parameter sharing within Bayesian Networks

I am learning about probabilistic graphical models and am a bit confused by the idea of parameter sharing. In the image below, I have been told that the parameters of time slice 0 are copied to time ...
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2answers
26 views

What are possible approaches for learning causal DAG of events?

I have historical data of event logs. Each event has an associated contextual Id, which can be used to tell that event A happened first in some context, then event B happened in same context and then ...
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28 views

How to interpret graphical model for Dirichlet process mixture for variational inference?

I am working through this paper by Blei and Jordan, which introduces variational inference for Dirichlet process mixtures. They derive an evidence lower bound (ELBO) function based on a stick breaking ...
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1answer
71 views

Gaussian Bayesian Networks and covariance calculation

I have difficulties in understanding the way of calculation of covariance matrix in Gaussian Bayesian Nets (from conditional to joint): The last formula is about to calculates covariance between ...
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0answers
37 views

Bayesian network- count probability of an event

I'm having hard time figuring out why the probability of $l^1 = 0.502$ (via https://courses.csail.mit.edu/6.034s/handouts/spring12/independencies.pdf) I'm getting quite lost in computations. Can ...