actually added bootstrap docs

docs/src/bootstrap.md

@@ -0,0 +1,56 @@
+# Bootstrap Filter
+
+The [`BootstrapFilter`](@ref) is the simplest filter provided by the library and should be the starting point for most tasks.
+
+## Quick Start
+
+With a POMDPs.jl model, setup looks like this:
+```jldoctest intro; output=false, filter=r"BasicParticleFilter.*" => s"BasicParticleFilter"
+using ParticleFilters, POMDPModels
+
+pomdp = TigerPOMDP()
+pf = BootstrapFilter(pomdp, 10)
+
+# output
+
+BasicParticleFilter()
+```
+
+Without POMDPs.jl, setup looks like this:
+```jldoctest intro; output=false, filter=r"BasicParticleFilter.*" => s"BasicParticleFilter"
+using ParticleFilters
+using Distributions: Normal
+
+dynamics(x, u, rng) = x + u + randn(rng)
+y_likelihood(x_previous, u, x, y) = pdf(Normal(), y - x)
+pf = BootstrapFilter(dynamics, y_likelihood, 10)
+
+# output
+
+BasicParticleFilter()
+
+```
+
+Once the filter has been created the [`update`](@ref) function can be used to perform a particle filter update.
+```jldoctest intro; output=false, filter=r"WeightedParticleBelief.*" => s"WeightedParticleBelief"
+b = ParticleCollection([1.0, 2.0, 3.0, 4.0])
+u = 1.0
+y = 3.0
+
+b_new = update(pf, b, u, y)
+
+# output
+
+WeightedParticleBelief()
+```
+
+## More on the Bootstrap Filter
+
+The [`BootstrapFilter`](@ref) is designed to be a sensible default choice for starting with particle filtering. The basic bootstrap filter approach was first described in "Novel approach to nonlinear / non-Gaussian Bayesian state estimation" by Gordon, Salmond, and Smith.
+The version in this package first checks whether the normalized [effective sample size](@ref effective_sample_size) of the particle belief is above a threshold (the `resample_threshold` argument). If it is below the threshold, the belief is resampled using [`low_variance_sample`](@ref). The particles are then propagated through the dynamics model and weighted by the likelihood of the observation. 
+
+The `BootstrapFilter` offers a modest level of customization. The most common need for customization is recovering from particle depletion. For this case, use the `postprocess` keyword argument to specify a function that can be used to check for depletion and recover. See the [Handling Particle Depletion](@ref) section for more information about this task. If more customization is needed, users should use the [`BasicParticleFilter`](@ref).
+
+```@docs
+BootstrapFilter
+```