API
Samplers
AdvancedPS introduces a few samplers extending AbstractMCMC. The sample
method expects a custom type that subtypes AbstractMCMC.AbstractModel
. The available samplers are listed below:
SMC
AdvancedPS.SMC
— TypeSMC(n[, resampler = ResampleWithESSThreshold()])
SMC(n, [resampler = resample_systematic, ]threshold)
Create a sequential Monte Carlo (SMC) sampler with n
particles.
If the algorithm for the resampling step is not specified explicitly, systematic resampling is performed if the estimated effective sample size per particle drops below 0.5.
The SMC sampler populates a set of particles in a AdvancedPS.ParticleContainer
and performs a AdvancedPS.sweep!
which propagates the particles and provides an estimation of the log-evidence
sampler = SMC(nparticles)
chains = sample(model, sampler)
Particle Gibbs
AdvancedPS.PG
— TypePG(n, [resampler = ResampleWithESSThreshold()])
diff --git a/dev/example/index.html b/dev/example/index.html
index 0a8a622..c1d65e0 100644
--- a/dev/example/index.html
+++ b/dev/example/index.html
@@ -255,7 +255,7 @@
Tutorials
-
-
Library API
+ Libraries
- Modellinglanguages
@@ -454,5 +454,6 @@
});
+
Examples
The following pages walk you through some examples using AdvancedPS and the Turing ecosystem.
Settings
This document was generated with Documenter.jl version 0.27.25 on Thursday 4 July 2024. Using Julia version 1.10.4.