bayesian-networks

问题 Is the are java alternative to Bayesian Belief Network framework - Infer.NET? Preferable if it be scalable(online learning for large datasets), well-supported(last updated since 2010) and open source and easy to write network structure. So all features from Infer.NET. 回答1: Listing here two Bayesian Network Classifiers projects that I am aware of: CI Bayes - active, last release is 2.0, released on 6th of Oct 2010 and available from their Maven2 repository jBNC - inactive for several years,

问题 I'm using Bayesian Network Repository (http://www.bnlearn.com/bnrepository/gaussian-medium.html#ecoli70) to obtain several datasets. To learn a Bayesian network structure, I need the observational data from each dataset. I'm not sure if Bayesian Network Repository includes this information or not. If it does, my question is how I can extract observational data from the datasets given in Bayesian Network Repository dataset. The observational data is in the form of a matrix of size n by p where

问题 As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance. Closed 7 years ago . Hello fellow Number crunchers As the headline suggests, I am looking for a library for learning and inference of Bayesian Networks. I

问题 this is the deal. So I have created a BN following the instructions from the BNT manual, is the sprinkler one but I have added a node Class for Winter and Summer. Like this: Cloudy------ / \ | Sprinkler Rain | \ / | | Wet Class Where class depends only on wether is cloudy or raining. With the same specification as http://bnt.googlecode.com/svn/trunk/docs/usage.html#basics And the class is also binary, the table is: C R Class prob --------------- 1 1 1 0 2 1 1 0.4 1 2 1 0.4 2 2 1 0.9 etc. So

问题 I am trying to build a Dynamic Bayesian Network using the BayesServer library in C# for my Unity3D game. I have the following method which implements the network // numberOfDistractors and levelId will be used later for added complexity in modeling void InitializeNetworkForLevel(int numberOfDistractors, int levelId) { beliefNet = new BayesServer.Network(); // add a knowledge node which is a latent variable (parameter to be learned from observed values KTrue = new State("KTrue"); KFalse = new

问题 I have a very quick question with an easy reproducible example that is related to my work on prediction with bnlearn library(bnlearn) Learning.set4=cbind(c("Yes","Yes","Yes","No","No","No"),c(9,10,8,3,2,1)) Learning.set4=as.data.frame(Learning.set4) Learning.set4[,c(2)]=as.numeric(as.character(Learning.set4[,c(2)])) colnames(Learning.set4)=c("Cause","Cons") b.network=empty.graph(colnames(Learning.set4)) struct.mat=matrix(0,2,2) colnames(struct.mat)=colnames(Learning.set4) rownames(struct.mat)

问题 How can one use pymc to parameterize a probabilistic graphical model? Suppose I have a PGM with two nodes X and Y . Lets say X->Y is the graph. And X takes two values {0,1} , and Y also takes two values {0,1} . I want to use pymc to learn the parameters of the distribution and populate the graphical model with it for running inferences. The way I could think of is as follows: X_p = pm.Uniform("X_p", 0, 1) X = pm.Bernoulli("X", X_p, values=X_Vals, observed=True) Y0_p = pm.Uniform("Y0_p", 0, 1)

问题 I am trying to build a Bayesian network model. However I am unable to install a suitable package. Tried gRain , bnlearn and Rgraphviz for plotting. I have tried in R 2.15 and 3.2 Following are the error messages : library(gRain) Loading required package: gRbase Loading required package: graph Error: package ‘graph’ could not be loaded In addition: Warning message: In library(pkg, character.only = TRUE, logical.return = TRUE, lib.loc = lib.loc) : there is no package called ‘graph’ > install

问题 Is there any API for building bayesian networks of influence diagram with java? 回答1: I've found two influence diagram engines with Java API which are free & open source: http://genie.sis.pitt.edu/ SMILE (Structural Modeling, Inference, and Learning Engine) is a fully portable library of C++ classes implementing graphical decision-theoretic methods, such as Bayesian net-works and influence diagrams, directly amenable to inclusion in intelligent systems. http://sourceforge.net/projects/unbbayes

问题 I want to use Bayesian Network mechanism for spam filtering. How do you think it should look a proper topology of the network? What about naive Bayes model? (The naive Bayes model is sometimes called a Bayesian classifier) 回答1: If you're new to spam filtering, it'd be a good idea to start with something simple like a naive Bayesian classifier. That way you get familiar with the issues involved in handling the data (reading the email, classifying it, storing your lexicon, etc.) without getting