Examples of PriorSample

aima.core.probability.bayes.approx.PriorSample
Artificial Intelligence A Modern Approach (3rd Edition): page 531.
```
 function PRIOR-SAMPLE(bn) returns an event sampled from the prior specified by bn inputs: bn, a Bayesian network specifying joint distribution P(X₁,...,X_n) x <- an event with n elements foreach variable X_i in X₁,...,X_n do x[i] <- a random sample from P(X_i | parents(X_i)) return x 
```
Figure 14.13 A sampling algorithm that generates events from a Bayesian network. Each variable is sampled according to the conditional distribution given the values already sampled for the variable's parents. @author Ciaran O'Reilly @author Ravi Mohan

Examples of aima.core.probability.bayes.approx.PriorSample

    BayesianNetwork bn = BayesNetExampleFactory
        .constructCloudySprinklerRainWetGrassNetwork();
    MockRandomizer r = new MockRandomizer(
        new double[] { 0.5, 0.5, 0.5, 0.5 });


    PriorSample ps = new PriorSample(r);
    Map<RandomVariable, Object> event = ps.priorSample(bn);


    Assert.assertEquals(4, event.keySet().size());
    Assert.assertEquals(Boolean.TRUE, event.get(ExampleRV.CLOUDY_RV));
    Assert.assertEquals(Boolean.FALSE, event.get(ExampleRV.SPRINKLER_RV));
    Assert.assertEquals(Boolean.TRUE, event.get(ExampleRV.RAIN_RV));

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Examples of aima.core.probability.bayes.approx.PriorSample

    BayesianNetwork bn = BayesNetExampleFactory
        .constructCloudySprinklerRainWetGrassNetwork();
    AssignmentProposition[] e = new AssignmentProposition[] { new AssignmentProposition(
        ExampleRV.SPRINKLER_RV, Boolean.TRUE) };
    MockRandomizer r = new MockRandomizer(new double[] { 0.1 });
    RejectionSampling rs = new RejectionSampling(new PriorSample(r));


    double[] estimate = rs.rejectionSampling(
        new RandomVariable[] { ExampleRV.RAIN_RV }, e, bn, 100)
        .getValues();

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Examples of aima.core.probability.bayes.approx.PriorSample


        ma[i + 3] = 0.1; // i.e. WetGrass=true
      }
    }
    MockRandomizer r = new MockRandomizer(ma);
    RejectionSampling rs = new RejectionSampling(new PriorSample(r));


    double[] estimate = rs.rejectionSampling(
        new RandomVariable[] { ExampleRV.RAIN_RV }, e, bn, 100)
        .getValues();

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