sb_probdsl offers simple discrete probabilistic programming support using scala's new delimited continuations support.
For compiling scala_probdsl you will need sbt.
Further library dependencies to install using "sbt publish-local" before compiling scala_probdsl:
compile scala_probdsl:
sbt compile
See Examples TODO:
TODO: this section is a little outdated. better see source
Instead of computing the probability distributions directly an unevaulated decision tree is build and only the root is returned.
On that tree different evaluation strategies may be applied. Implemented so far are:
prob[A] { ... } // will evaluate tree to full decision tree
normalizedProb[A] { ... } // like prob[A], but will apply
// Probability.normalize to distribution
// which must have the type
// Distribution[Option[A]].
// usefull when doing bayesian inference
pickValue[A](tree) // randomly samples a value from unevaluated tree.
// This is linear in the number of random
// variables to be visited.
collect[A](pred, tree) // logically samples values from unevaluated tree
// until the given predicate returns false.
// Due to the fact, that the tree is build lazily
// sampling a value from tree is O(N) with N being
// the number random variables to visit.
collecting[A](pred) { ... } // uses collect to evaluate given context
loopK(k), loopMaxMs(time) // predefined predicates to be used with
// collect/collecting evaluators
Some examples can be found in "examples/Test1.scala"
In order to use the examples in the scala REPL, you just need to load them into the scala console startet using "sbt console" and call the example its "run" method:
$ sbt console
scala> :load examples/MontyHall.scala
Loading examples/MontyHall.scala...
defined module MontyHall
scala> MontyHall.run
...
scala> :load examples/DrugTest.scala
Loading examples/DrugTest.scala...
defined module DrugTest
scala> DrugTest.run
...
Recommended example reading order:
- examples/Test1.scala # just some very basic experiments
- examples/Diagnosis.scala # most basic bayesian inference example
- examples/MontyHall.scala # monty hall problem/paradox
- examples/Alarm.scala # example from Artificial Intelligence - A Modern Approach
- example/SpamPlan.scala # a (not complete) spam filter