| Safe Haskell | None |
|---|---|
| Language | Haskell2010 |
Bayes.Sampling
Description
Sampling
Samplers for Bayesian network inferences
- data Sampler g a = forall b . Sampler !b !(GenIO -> IO (Sample g a)) !(GenIO -> SamplerGraph g a) !(SamplingScheme g b a)
- type Sample g a = BayesianNetwork g a
- runSampling :: (DirectedGraph g, FunctorWithVertex g) => Int -> Int -> Sampler g a -> IO [Sample g a]
- topologicalOrder :: DirectedGraph g => g a b -> [Vertex]
- discreteAncestralSampler :: (Factor f, FunctorWithVertex g, DirectedGraph g) => BayesianNetwork g f -> Sampler g DVI
- gibbsSampler :: (Factor f, FunctorWithVertex g, DirectedGraph g) => BayesianNetwork g f -> [DVI] -> Sampler g DVI
- gibbsMCMCSampler :: (Factor f, FunctorWithVertex g, DirectedGraph g) => BayesianNetwork g f -> [DVI] -> Sampler g DVI
- samplingHistograms :: (InstantiationValue i v, BayesianVariable i, FunctorWithVertex g, Graph g) => Int -> [Sample g i] -> Sample g [(Double, Double, Double)]
- histogram :: Int -> [Double] -> [(Double, Double, Double)]
- type ContinuousNetwork = SBN Distri
- type ContinuousSample = SBN CVI
- data Distri = D !CV !(DistributionF DirectedSG (Double, Double) CVI)
- continuousMCMCSampler :: ContinuousNetwork -> [CVI] -> Sampler DirectedSG CVI
Types
Sampler defining the behavior of a sampling algorithms (init value, sample generation, how to select nodes in the grapg)
type Sample g a = BayesianNetwork g a Source
A sample (graph of instantiations)
Sampling
Arguments
| :: (DirectedGraph g, FunctorWithVertex g) | |
| => Int | Number of used samples |
| -> Int | Burnin samples before the used samples |
| -> Sampler g a | Sampler |
| -> IO [Sample g a] | List of generated samples |
Sample a bayesian network using a given sampling scheme
topologicalOrder :: DirectedGraph g => g a b -> [Vertex] Source
Return the vertices in topological order
Sampling schemes
discreteAncestralSampler :: (Factor f, FunctorWithVertex g, DirectedGraph g) => BayesianNetwork g f -> Sampler g DVI Source
Ancestral sampler which does not support evidence
Arguments
| :: (Factor f, FunctorWithVertex g, DirectedGraph g) | |
| => BayesianNetwork g f | Bayesian network |
| -> [DVI] | Evidence |
| -> Sampler g DVI | Sampler |
Gibbs sampling
Arguments
| :: (Factor f, FunctorWithVertex g, DirectedGraph g) | |
| => BayesianNetwork g f | Bayesian network |
| -> [DVI] | Evidence |
| -> Sampler g DVI | Sampler |
Gibbs sampling
Sampling results
Arguments
| :: (InstantiationValue i v, BayesianVariable i, FunctorWithVertex g, Graph g) | |
| => Int | Number of bins |
| -> [Sample g i] | Samples |
| -> Sample g [(Double, Double, Double)] | Histogram with start bins and bin content |
Generate a graph of sampling histogram for each variable So, for a vertex v we have the posterior values p(v)
Arguments
| :: Int | Bins |
| -> [Double] | Samples |
| -> [(Double, Double, Double)] | Histogram (minBound,maxBound, value) |
Compute the histogram of values
For continuous distributions
type ContinuousNetwork = SBN Distri Source
type ContinuousSample = SBN CVI Source
Arguments
| :: ContinuousNetwork | Bayesian network |
| -> [CVI] | Evidence |
| -> Sampler DirectedSG CVI | Sampler |
Gibbs sampling for continuous network