SequentialMonteCarlo.jl presents a set of particle filters and Sequential Monte Carlo algorithms for both latent state and model parameter inference.
To begin using this module, we must first properly establish the construction of state space models (SSMs) under my framework. Typical model construction requires a small set of constructors for: (1) an initial distribution, (2) a transition density, and (3) an observation density.
To construct a univariate linear Gaussian SSM, we can define the following constructor, where θ::Vector{Float64} represents a vector of parameters.
lg_mod(θ) = StateSpaceModel(
LinearGaussian(θ[1],1.0,θ[2],θ[3],0.0),
(1,1)
)Calling lg_mod(⋅) constructs a StateSpaceModel object, which can be called to simulate data, or to be used in a particle filter. As long as the model in question has the required methods defined. For demonstration define lg_example and simulate 100 periods.
lg_example = lg_mod([0.5,0.9,0.8])
_,y = simulate(lg_example,100)This construction weighs heavily on multiple dispatch, which is a nice feature of Julia but also requires more work from the user. As such, a better method is in the works.
Particle filters are primarily used for online inference for latent states, which is ideally defined as a mutating operation given a set of particles x and weights w.
Suppose we observe a vector y simulated from model lg_example. To construct a bootstrap filter with 1024 particles and capture summary statistics at each time period we do the following:
# preallocate quantile vector and log likelihood
xq = fill(zeros(Float64,3),length(y))
logZ = 0.0
# initialize bootstrap filter at time t=1
x,w,logμ = bootstrap_filter(1024,y[1],lg_example)
xq[1] = quantile(x,[0.25,0.5,0.75])
logZ += logμ
# subsequent iterations of the bootstrap filter
for t in 2:eachindex(y)
# run filter and print ess
logμ,w,ess = bootstrap_filter!(x,w,y[t],lg_example)
@printf("t = %4d\tess = %4.3f",t,ess)
# update summary statistics
xq[t] = quantile(x,[0.25,0.5,0.75])
logZ += logμ
endIt should be noted that x and w are both operated in-place and thus update with each call of bootstrap_filter!(). Furthermore, to avoid such long winded code, one can instead call the function log_likelihood() to run a particle filter over all periods of a model and return the log likelihood (or logZ as its referred to above).
# for a bootstrap filter leave the proposal argument empty
logZ = log_likelihood(1024,y,lg_example)The construction of these particle filters is not perfect on its own, since it is meant specifically to perform joint estimation. However, they are fully functional and meant to be flexible enough when called in high volumes. Something that LowLevelParticleFilters.jl actually fails to do (more on this in issue...).
Suppose a given state space model has an uncertain construction such that the model parameters are unobserved. Now the problem becomes twofold: what can we infer about the latent states as well as the parameters?
To solve this problem, I introduce two main algorithms: SMC² and density tempered SMC.
Running these algorithms requires a prior for the parameter space as well as a model constructor as we defined above with lg_mod(). Below we define lg_prior such that length(lg_prior) == length(θ) where θ is the input to the function lg_mod.
# model constructor
lg_mod(θ) = StateSpaceModel(
LinearGaussian(θ[1],1.0,θ[2],θ[3],0.0),
(1,1)
)
# prior definition
lg_prior = product_distribution([
TruncatedNormal(0,1,-1,1),
LogNormal(),
LogNormal()
])Upon declaration of the prior and model constructor, we define a generic SMC sampler with 513 parameter particles, 1024 state particles, and ESS threshold of 0.5, and 3 MCMC steps. To demonstrate, we can run density tempered SMC like so...
lg_dt_smc = SMC(512,1024,lg_mod,lg_prior,3,0.5)
density_tempered(lg_dt_smc,y)For an online algorithm like SMC², we treat it similarly to the particle filter by using mutating functions to change properties of lg_smc² as time progresses.
lg_smc² = SMC(512,1024,lg_mod,lg_prior,3,0.5)
smc²(lg_smc²,y)
for t in 2:T
smc²!(lg_smc²,y,t)
end
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