README.md

KissABC

Table of Contents

• Beginners Usage Guide
• Details

Usage guide

The ingredients you need to use Approximate Bayesian Computation:

1. A simulation which depends on some parameters, able to generate datasets similar to your target dataset if parameters are tuned
2. A prior distribution over such parameters
3. A distance function to compare generated dataset to the true dataset

We will start with a simple example, we have a dataset generated according to an Normal distribution whose parameters are unknown

tdata=randn(1000).*0.04.+2

we are ofcourse able to simulate normal random numbers, so this constitutes our simulation

sim((μ,σ), param) = randn(1000) .* σ .+ μ

The second ingredient is a prior over the parameters μ and σ

using Distributions
using KissABC
prior=Factored(Uniform(1,3), Truncated(Normal(0,0.1), 0, 100))

we have chosen a uniform distribution over the interval [1,3] for μ and a normal distribution truncated over ℝ⁺ for σ.

Now all that we need is a distance function to compare the true dataset to the simulated dataset, for this purpose a Kolmogorov-Smirnoff distance is good

using StatsBase
function ksdist(x,y)
    p1=ecdf(x)
    p2=ecdf(y)
    r=[x;y]
    maximum(abs.(p1.(r)-p2.(r)))
end

Now we are all set, first we define an ABCplan via

plan = ABCplan(prior, sim, tdata, ksdist)

where ofcourse the four parameters are the ingredients we defined earlier in the previous steps, and then we can use ABCSMCPR which is sequential Monte Carlo algorithm to simulate the posterior distribution for this model

res,Δ = ABCSMCPR(plan, 0.1, nparticles=200,parallel=true)

Or, we can use ABCDE which is still an SMC algorithm, but with an adaptive proposal, which is much more efficient

res,Δ = ABCDE(plan, 0.1, nparticles=200,parallel=true)

In any case we chose a tolerance on distances equal to 0.1, a number of simulated particles equal to 200, we enabled Threaded parallelism, and the simulated posterior results are in res, while in Δ we can find the distances calculated for each sample. We can now extract the results:

prsample=[rand(prior) for i in 1:5000] #some samples from the prior for comparison
μ_pr=getindex.(prsample,1) # μ samples from the prior
σ_pr=getindex.(prsample,2) # σ samples from the prior
μ_p=getindex.(res,1) # μ samples from the posterior
σ_p=getindex.(res,2) # σ samples from the posterior

and plotting prior and posterior side by side we get:

we can see that the algorithm has correctly inferred both parameters, this exact recipe will work for much more complicated models and simulations, with some tuning.

Details

This package currently implements 3 algorithms whose details can be found in Docs

1. ABC this is a standard rejection algorithm, you can find examples in test/runtests.jl
2. ABCSMCPR this is the sequential monte carlo algorithm by Drovandi et al. 2011, you can find an examples in test/runtests.jl
3. ABCDE this is the population monte carlo algorithm based on differential evolution by B.M. Turner 2012, you can find an examples in test/runtests.jl
4. KABCDE this is the kernel sequential monte carlo algorithm based on differential evolution by B.M. Turner 2012, you can find an examples in test/runtests.jl (this algorithm tends to be the most sample efficient)