Merge branch 'master' into fred/extension

examples/particle-gibbs/script.jl

@@ -1,4 +1,3 @@
-# # Particle Gibbs for non-linear models
 using AdvancedPS
 using Random
 using Distributions
@@ -13,34 +12,33 @@ using Libtask
 # x_{t+1} = a x_t + v_t \quad v_{t} \sim \mathcal{N}(0, r^2)
 # ```
 # ```math
-# y_{t} = e_t \exp(\frac{1}{2}x_t) \quad v_{t} \sim \mathcal{N}(0, 1)
+# y_{t} = e_t \exp(\frac{1}{2}x_t) \quad e_t \sim \mathcal{N}(0, 1) 
 # ```
 #
-# Here we assume the static parameters $\theta = (q^2, r^2)$ are known and we are only interested in sampling from the latent state $x_t$.
 # We can reformulate the above in terms of transition and observation densities:
 # ```math
-# x_{t+1} \sim f_{\theta}(x_{t+1}|x_t) = \mathcal{N}(a x_t, q^2)
+# x_{t+1} \sim f_{\theta}(x_{t+1}|x_t) = \mathcal{N}(a x_t, r^2)
 # ```
 # ```math
 # y_t \sim g_{\theta}(y_t|x_t) = \mathcal{N}(0, \exp(\frac{1}{2}x_t)^2)
 # ```
 # with the initial distribution $f_0(x) = \mathcal{N}(0, q^2)$.
+# Here we assume the static parameters $\theta = (q^2, r^2)$ are known and we are only interested in sampling from the latent state $x_t$. 
 Parameters = @NamedTuple begin
  a::Float64
  q::Float64
  T::Int
 end
 
-mutable struct NonLinearTimeSeries <: AbstractMCMC.AbstractModel
+mutable struct NonLinearTimeSeries <: AdvancedPS.AbstractStateSpaceModel
  X::Array
  θ::Parameters
  NonLinearTimeSeries(θ::Parameters) = new(zeros(Float64, θ.T), θ)
 end
 
 f(model::NonLinearTimeSeries, state, t) = Normal(model.θ.a * state, model.θ.q)
-g(model::NonLinearTimeSeries, state, t) = Normal(0, exp(0.5 * state)^2)
+g(model::NonLinearTimeSeries, state, t) = Normal(0, exp(0.5 * state))
 f₀(model::NonLinearTimeSeries) = Normal(0, model.θ.q)
-#md nothing #hide
 
 # Let's simulate some data
 a = 0.9 # State Variance
@@ -88,8 +86,8 @@ end
 
 # Here we use the particle gibbs kernel without adaptive resampling.
 model = NonLinearTimeSeries(θ₀)
-pgas = AdvancedPS.PG(Nₚ, 1.0)
-chains = sample(rng, model, pgas, Nₛ; progress=false);
+pg = AdvancedPS.PG(Nₚ, 1.0)
+chains = sample(rng, model, pg, Nₛ; progress=false);
 #md nothing #hide
 
 # Each sampled trajectory holds a NonLinearTimeSeries model
@@ -98,8 +96,7 @@ mean_trajectory = mean(particles; dims=2)
 #md nothing #hide
 
 # We can now plot all the generated traces.
-# Beyond the last few timesteps all the trajectories collapse into one. Using the ancestor updating step can help
-# with the degeneracy problem.
+# Beyond the last few timesteps all the trajectories collapse into one. Using the ancestor updating step can help with the degeneracy problem, as we show below.
 scatter(particles; label=false, opacity=0.01, color=:black, xlabel="t", ylabel="state")
 plot!(x; color=:darkorange, label="Original Trajectory")
 plot!(mean_trajectory; color=:dodgerblue, label="Mean trajectory", opacity=0.9)
@@ -119,3 +116,35 @@ plot(
  ylabel="Update rate",
 )
 hline!([1 - 1 / Nₚ]; label="N: $(Nₚ)")
+
+# Let's see if ancestor sampling can help with the degeneracy problem. We use the same number of particles, but replace the sampler with PGAS. 
+# To use this sampler we need to define the transition and observation densities as well as the initial distribution in the following way:
+AdvancedPS.initialization(model::NonLinearTimeSeries) = f₀(model)
+AdvancedPS.transition(model::NonLinearTimeSeries, state, step) = f(model, state, step)
+function AdvancedPS.observation(model::NonLinearTimeSeries, state, step)
+ return logpdf(g(model, state, step), y[step])
+end
+AdvancedPS.isdone(::NonLinearTimeSeries, step) = step > Tₘ
+
+# We can now sample from the model using the PGAS sampler and collect the trajectories.
+pg = AdvancedPS.PGAS(Nₚ)
+chains = sample(model, pg, Nₛ);
+particles = hcat([trajectory.model.f.X for trajectory in trajectories]...)
+mean_trajectory = mean(particles; dims=2)
+
+# The ancestor sampling has helped with the degeneracy problem and we now have a much more diverse set of trajectories, also at earlier time periods.
+scatter(particles; label=false, opacity=0.01, color=:black, xlabel="t", ylabel="state")
+plot!(x; color=:darkorange, label="Original Trajectory")
+plot!(mean_trajectory; color=:dodgerblue, label="Mean trajectory", opacity=0.9)
+
+# The update rate is now much higher throughout time.
+update_rate = sum(abs.(diff(particles; dims=2)) .> 0; dims=2) / Nₛ
+plot(
+ update_rate;
+ label=false,
+ ylim=[0, 1],
+ legend=:bottomleft,
+ xlabel="Iteration",
+ ylabel="Update rate",
+)
+hline!([1 - 1 / Nₚ]; label="N: $(Nₚ)")