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// Copyright ©2015 The Gonum Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.
package sampleuv_test
import (
"gonum.org/v1/gonum/stat/distuv"
"gonum.org/v1/gonum/stat/sampleuv"
)
type ProposalDist struct {
Sigma float64
}
func (p ProposalDist) ConditionalRand(y float64) float64 {
return distuv.Normal{Mu: y, Sigma: p.Sigma}.Rand()
}
func (p ProposalDist) ConditionalLogProb(x, y float64) float64 {
return distuv.Normal{Mu: y, Sigma: p.Sigma}.LogProb(x)
}
func ExampleMetropolisHastings_burnin() {
n := 1000 // The number of samples to generate.
burnin := 50 // Number of samples to ignore at the start.
var initial float64
// target is the distribution from which we would like to sample.
target := distuv.Weibull{K: 5, Lambda: 0.5}
// proposal is the proposal distribution. Here, we are choosing
// a tight Gaussian distribution around the current location. In
// typical problems, if Sigma is too small, it takes a lot of samples
// to move around the distribution. If Sigma is too large, it can be hard
// to find acceptable samples.
proposal := ProposalDist{Sigma: 0.2}
samples := make([]float64, n)
mh := sampleuv.MetropolisHastings{Initial: initial, Target: target, Proposal: proposal, BurnIn: burnin}
mh.Sample(samples)
}
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