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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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How to determine if two directed probabilistic graphical models are I-equivalent?

I'm trying to figure out how to determine if these two models are I-equivalent. Google didn't properly give me a solid answer so far. Any idea on to determine it? Thank you.
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Machine learning to estimate p(y>N | X)

To illustrate, let's say I have a mobile game and I want to predict the duration of each session $y$ when they start. Say I have a training set with multiple useful features $X$ from previous ...
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How to approximately sample an unknown non-parametric joint distribution given a complete set of partial conditional distributions?

This question is related somewhat to Bayesian networks. In a BN, you have a DAG (directed acyclic graph). By supplying the root nodes with a sample, you can then follow the directed arcs to sample ...
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How to use Bayesian belief Network map/Causality map for segmentation?

I have obtained the causality map for my data. I have an event of interest and the evidences for the event. How do I make the use of this information to come up with segmentation/clustering such that ...
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Log or MSE loss for hyperparameter tuning of probabilistic NN

I am building a predictive model of a dynamical system using a NN whose output neurons enconde the mean and diagonal covariance of a Gaussian distribution. For training, the negative log prediction ...
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Bayes R2 computation

I am working on evaluating the performance of a Bayesian network. One of the metrics I'm considering is the Bayes R-squared. On going through this publication, http://www.stat.columbia.edu/~gelman/...
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Conditional Independence in Bayes Network

I have just started working through Michael Jordan's notes on Probabilistic Graphical Models and seem to be stuck on the exercise on page 5. I summarize the question here: Suppose $G = (V,E)$ is a ...
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forward sampling for Bayesian network with continuous variables and equation-based causal relationships

I have a physical system which can be represented by the following Bayesian network. It has the following characteristics 1) The encoded variables are continuous variables 2) The causal ...
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Bayesian network with continuous variable data set in python

I am looking to predict continuous target lets say Sales with KPIs like price distribution using Bayesian network. I have got it done with BNlearn in R but still ...
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Bayesian estimation of traffic flow - Help with methodology

I need help setting up a model for estimation of traffic flow. I shall do the analysis with a Bayesian approach. Data: I have sensor data from ten sensors. The sensors are installed at three main ...
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probabilistic distribution of a variable which are based on random imputs

In practice, I have a variable x, which is based on (b,c,d). We may have a physics based math formula to describe the relationship between x and (b,c d), i.e., x=f(b,c,d). Beforehand, we may know the ...
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Find Conditional probability distribution given conditions - Bayes Network

I'm taking a course on bayesian statistics and I'm having trouble with one assigment, it goes like this: Construct an example in which two variables have a common effect, and the presence of one of ...
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If I(G) = I(G') do they have the same skeleton and the same v-strucures except those in complete (sub-) graph?

I thought if two different graphs G, G' have the same skeleton and the same v-structure, then I(G)=I(G′) and I(G)≠∅. But does the converse also holds? In this case a complete graph doesn't apply ...
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Do all P-Maps for a distribution share the same skeleton or/and v-structures?

I learned that a distribution may have multiple P-Maps because even if we are sure about the independences in a graph we cannot gurrantee the causal direction, then I just wonder if those P-Maps share ...
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Independence imply d-separation?

In the context of Bayesian networks if two random variables (i.e. nodes) are d-separated, they are independent. However, is there any example of random variables being independent but not d-separated?
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How to assign values over this two variables A and B in an apocryphal “Bayesian Network” like A->B->A?

Let's say that we have a graph A->B->C, and its coditional independence representation is like follows: ...
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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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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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2answers
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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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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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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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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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1answer
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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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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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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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68 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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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217 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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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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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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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
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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
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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
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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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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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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 ...