Description Slots Methods Author(s)
Basic class to implement a Sequential Monte Carlo sampler.
particles = "ANY", logWeights = "vector", # log of particle weights unifWeights = "logical", # are current weights uniform? p_move = "function", lW_update = "function", logLik = "function", resampleC = "numeric", N = "integer"
particles
:Set of particles. Format depends on implementation.
logWeights
:Vector containing the log (unnormalised) particle weights.
unifWeights
Logical indicating whether the logWeights are uniform.
p_move
:Function to move the particles to a new position.
mcmc_move
:Function to perform a Monte Carlo Markov Chain move.
lW_update
:Function to do update the logWeights
.
logLik
:Function to compute the logLik
.
resampleC
:Numeric value (between 0 and 1) indicating when to perform resampling.
N
:Integer indicating the total number of particles.
signature(object = "ParticleBase")
: return particles
signature(object = "ParticleBase")
: set particles
signature(object = "ParticleBase")
: move particles
#
signature(object = "ParticleBase")
: move particles
signature(object = "ParticleBase")
: perform a full SMC iteration
#
signature(object = "ParticleBase")
: move particles
signature(object = "ParticleBase")
: update weights
#
signature(object = "ParticleBase")
: move particles
signature(object = "ParticleBase")
: Effective Sample Size
signature(object = "ParticleBase")
: get the log of the particles weights
signature(object = "ParticleBase")
: set the log of the particles weights
#
signature(object = "ParticleBase")
: move particles
#
signature(object = "ParticleBase")
: move particles
signature(object = "ParticleBase")
: get the exponentiated (unnormalized) weights
signature(object = "ParticleBase")
: get the normalized weights
Maarten Speekenbrink
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