- Join over
**1.5M+ people** - Join over
**100K+ communities** - Free
**without limits** - Create
**your own community**

I've added my associate who will be very happy to see this ;-)

The Sprinkler example still does not work due to danielkorzekwa/bayes-scala#11, right? Maybe that issue should be left open, so that it's more obvious that this is not fixed yet.

Is any release planned?

two examples on EM in discrete multinomial bayesian nets: https://github.com/danielkorzekwa/bayes-scala/blob/master/doc/clustergraph/clustergraph.md

Getting Started - Learning parameters with Expectation Maximisation in Bayesian Networks from incomplete data

Getting Started - Learning parameters with Expectation Maximisation in Unrolled Dynamic Bayesian Networks from incomplete data

the second example show parameters tying

with max two parents

I never really optimized it and moved quite soon to hybrid bayesian nets quite soon

still a few people used it for some use cases, e.g. biology and medicine and it worked, just give a go

@danielkorzekwa Is this model based on a paper or just an example? https://github.com/danielkorzekwa/bayes-scala/blob/master/doc/dsl/conversionrate/conversion_rate.md

regarding to categorical parents, unfortunately my package does not support it. It is best suited either for discrete models only or for Gaussian Process like models ( Gaussian Process Regression, GPC, Modelling skills in games, large scale GP (e.g. hierarchical, etc.), GP like models are now inside of bayes-scala-gp.

Hi Daniel, thanks for this great piece of work. I'm just familiar enough with Bayesian networks to have made sense of your ClusterGraph examples. Is it possible to obtain a posterior joint marginal for a subset of the variables? I was looking for something like "loopyBP.marginal(var1.id, var2.id,...)" Is there capability like that already in the program? If not, does it make sense for me to dig in and figure out how to add the capability? Or is there something in the loopy algorithm or cluster graph structure that makes that not possible? (I'm familiar with bucket elimination, not with loopy BP, nor with cluster graphs... but I bought the Koller book to learn about them)

You can add P(A,B) to cluster graph or to factor graph so that you have a join variable. Second option, If A and B are separate variables you can multiply their posteriors in order to get a joint distribution but here you of course make some assumptions about A and B being independent.

I have had the problem to compile the project with sbt. I got error message that org.scoverage#sbt-scoverage;1/04: not found and unresolved dependncy: com.codacy#sbt-codacy-coverage; 1.0.3: not fount. Both of them are defined in the plugins.sbt. Does any one have the solution? thanks.

Hi Daniel. kakaner and I are working together. We have a question about representing our network as a cluster graph. The edges in our network are A -> B, B -> C, and A -> C, which we represent as two factors, factor 1 over (A, B), and factor 2 over (A, B, C). If we attempt to map each factor to its own cluster, we run into the single variable in supset limitation (with A and B both being in the supset). Is this a sensible cluster graph for our network? Can you suggest any workarounds?

Having (A,B) and (A,B,C) clusters makes no sense to me. If you put all variables to a single cluster you don't really need any other clusters as you already have a full joined probability distribution.

so I can see two approaches (if having a full joined distribution across (A,B,C) is a problem:

first on cluster graph with clusters (A,B) (B,C) and (A,C)

second approach almost? equivalent (small diffs during message passing ) on factor graphs with factors (A,B), (B,C) and (A,C)

honestly we don't have great intuition around how 3 two var factors differ from 1 3 var factor so are just kind of learning from experimentation

Hi @danielkorzekwa , I'm a student who is doing a research on Bayesian networks to analyze multinomial survey data. Currently I am using R as a programming language. Your tool bayes-scala looks spot on like what I'm looking for. However, I am having difficulties implementing it in R so I was hoping you might give a 101 Introduction on how to use the Scala program in an R environment. Thanks for your hard work and hope to hear from you.

though I never used it myself

or just try using Scala directly

btw, I'm not working on bayes-scala anymore, I changed companies and I can't support you with more detailed questions, sorry :)