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.
unifWeightsLogical 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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