Safe Haskell | Safe-Infered |
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Example of sampling

Two samplers are availables : the `discreteAncestralSampler`

and the `gibbsSampler`

.
Only the `gibbsSampler`

can be used with evidence.

In this example, we have a very simple network.

simple :: ([`TDV`

Bool],`SBN`

`CPT`

) simple =`runBN`

$ do a <-`variable`

"a" (`t`

:: Bool) b <-`variable`

"b" (`t`

:: Bool) --`proba`

a`~~`

[0.4,0.6]`cpt`

b [a]`~~`

[0.8,0.2,0.2,0.8] -- return [a,b]

This network is representing a sensor b. We observe the value of b and we want to infer the value of a.

We use the `gibbsSampler`

for this with an initial period of 200 samples which are dropped. The `gibbsSampler`

is
generate a stream of samples. From this stream, we need to compute a probability distribution. For this, we use
the `samplingHistograms`

histogram function which is generating a list : the probability values of each vertex.

let (vars@[a,b],exampleG) = simple n <-`runSampling`

5000 200 (`gibbsSampler`

exampleG [b`=:`

True]) let h =`samplingHistograms`

n print $ h

Then, we compare this result with the exact one we get with a junction tree.

let jt =`createJunctionTree`

`nodeComparisonForTriangulation`

exampleG jt' =`changeEvidence`

[b`=:`

True] jt mapM_ (x -> print .`posterior`

jt' $ [x]) vars

We can also use the `discreteAncestralSampler`

to compute the posterior but it is not supporting the use of evidence in this
version. The syntax is similar.

n <-`runSampling`

500 (`discreteAncestralSampler`

exampleG)

- testSampling :: IO ()

# Test function

testSampling :: IO ()Source