hbayes-0.2.1: Inference with Discrete Bayesian Networks

Safe HaskellSafe-Infered




Tutorial explaining how to make infereces with the library.

Thus tutorial is using examples from the module Bayes.Examples. Please, refer to this module for documentation about how the example bayesian networks are created or loaded.


The function inferencesOnStandardNetwork is showing how to use variable elimination and factor elimination to make inferences.

First, the example is loaded to make its variables and its bayesian network available:

    let ([winter,sprinkler,rain,wet,road],exampleG) = example

Then, we compute a prior marginal. Prior means that no evidence is used. A bayesian network is a factorisation of a distribution P(A B C ...). If you want to know the probability of only A, you need to sum out the other variables to eliminate them and get P(A). To compute this prior marginal using variable elimnation, you need to give an elimination order. The complexity of the computation is depending on the elimination order chosen.

For instance, if you want to compute the prior probability of rain, you can write:

    priorMarginal exampleG [winter,sprinkler,wet,road] [rain] 

Now, if you have observed that the grass is wet and want to take into account thios observation to compute the posterior probability of rain (after observation):

    posteriorMarginal exampleG [winter,sprinkler,wet,road] [rain]  [wet =: True]

If you want to combine several observations:

    posteriorMarginal exampleG [winter,sprinkler,wet,road] [rain]  [wet =: True, sprinkler =: True]

There are several problems with variable elimination:

  • You have to specify an elimination order
  • If you want to compute another marginal (for instance probability of winter), you have to recompute everything.

But, there exists another category of elimination algorithms based upon factor elimination. They require the creation of an auxiliary data structure : the junction tree.

This tree is then used for computing all marginals (without having to recompute everything). The junction tree is equivalent to giving an elimination order.

So, the previous examples can also be computed with factor elimination. First, the junction tree must created:

    let jt = createJunctionTree nodeComparisonForTriangulation exampleG

The junction tree being equivalent to an elimination order, the order chosen will depend on a cost function. In the previous example, the cost function nodeComparisonForTriangulation is used. Other cost functions may be introduced in a futute version of this library.

Once the junction tree has been computd, it can be used to compute several marginals:

    posterior jt rain

The function is called posterior and will compute posterior only when solme evidence has been introduced into the tree. Otherwise it is computing a prior.

To set evidence, you need to update the junction tree with new evidence:

    let jt' = updateEvidence [wet '=:'' True] jt 
    posterior jt' rain

Inferences with an imported network

There is a slight additional difficulty with imported networks : you need to create new data type to be able to set evidence.

For instance, in the cancer network there is a Coma variable with levels Present or Absent. When imported, those levels are imported as number. But, the evidence API in this library is requiring enumerations.

So, you need to create a Coma type:

    data Coma = Present | Absent deriving(Eq,Enum,Bounded)

and check that Present is corresponding to the level 0 in the importd network.

Once this datatype is created, you can easily use the cancer network. First we load the network and import the discrete variables of type DV from the names of the nodes in the network (not the label of the nodes)

    print "CANCER NETWORK"
    (varmap,cancer) <- exampleImport
    print cancer
    let [varA,varB,varC,varD,varE] = fromJust $ mapM (flip Map.lookup varmap) [A,B,C,D,E]

Once the variables are available, you can create the junction tree and start making inferences:

    let jtcancer = createJunctionTree nodeComparisonForTriangulation cancer
    mapM_ (x -> putStrLn (show x) >> (print . posterior jtcancer $ x)) [varA,varB,varC,varD,varE]
    let jtcancer' = updateEvidence [varD =: Present] jtcancer 
    mapM_ (x -> putStrLn (show x) >> (print . posterior jtcancer' $ x)) [varA,varB,varC,varD,varE]


Tests with the standard network

inferencesOnStandardNetwork :: IO ()Source

Inferences with the standard network

Tests with the cancer network

inferencesOnCancerNetwork :: IO ()Source

Inferences with the cancer network

data Coma Source

Type defined to set the evidence on the Coma variable from the cancer network.