Inference Methods

From Clojure, the syntax for calling an inference algorithm on a query is (doquery :method foo args & method-opts). For example, we could run the smc algorithm with :number-of-particles set to 100 on a program foo defined by (defquery foo [x] ...) as follows

(doquery :smc
foo [xval]
:number-of-particles 100)


When a query object is passed to doquery or conditional the user has a choice of what kind of inference should be used to perform the characterization of the denoted conditional distribution. In general we recommend rmh if all observes occur at the end of the program, smc with as many particles as can fit in memory if observes occur with the program, and ipmcmc for programs with internal observes that are too large for smc to solve well.

CRITICAL: the default arguments supplied for all inference methods are the minimum required. This design make interactive debugging easier but nearly absolutely ensures poor inference performance unless non-default parameter values are chosen. For instance smc performance will only get better with more particles; ultimately how many particles to use should be chosen so as to maximize the joint criteria of how long you want to wait and how good you want your results to be.

Anglican provides 4 general categories of inference methods and several instances of each:

• Importance sampling (IS),
• Markov chain Monte Carlo (MCMC),
• Particle Markov chain Monte Carlo (PMCMC),
• Maximum a posteriori (MAP).

The current list of algorithms that are included with Anglican is

Method Type Description
importance IS Importance sampling (likelihood weighting)
smc IS Sequential Monte Carlo
pcascade IS Particle cascade (asynchronous sequential Monte Carlo)
pgibbs PMCMC Particle Gibbs (iterated conditional SMC)
pimh PMCMC Particle independent Metropolis-Hastings
pgas PMCMC Particle Gibbs with ancestor sampling
ipmcmc PMCMC Interacting particle Markov chain Monte Carlo
lmh MCMC Lightweight Metropolis-Hastings
rmh MCMC Random-walk Lightweight Metropolis-Hastings
almh MCMC Adaptive scheduling lightweight Metropolis-Hastings
palmh MCMC Parallelised adaptive scheduling lightweight Metropolis-Hastings
plmh MCMC Parallelised lightweight Metropolis-Hastings
bamc MAP Bayesian Ascent Monte Carlo
siman MAP MAP estimation via simulated annealing
bbvb VI Black Box Variational Inference

Method-specific Options

Some algorithms have optional arguments, which can be specified with (doquery :method prog args :option value). These are

Method Keyword Default Description
smc :number-of-particles 1 Number of particles
rmh :alpha 0.5 Probability of using a local MCMC move
:sigma 1 Spread of the local move
ipmcmc :number-of-particles 2 Number of particles per sweep
:number-of-nodes 32 Number of nodes running SMC and CSMC
:number-of-csmc-nodes (/ :number-of-nodes 2) Number of nodes CSMC
:all-particles? true Return all particles or just one per node
:pool (+ (npus) 2) Number of threads to use
pgibbs :number-of-particles 2
pgas :number-of-particles 2
pimh :number-of-particles 2
plmh :number-of-threads 2 Number of processor threads
palmh :number-of-threads 2
pcascade :number-of-threads 16
:number-of-particles :number-of-threads/2 Initial number of particles
bamc :predict-candidates false Output samples with non-increasing log-weight
siman :predict-candidates false
:cooling-rate 0.99 Cooling rate (should be less than 1)
:cooling-schedule :exponential Cooling schedule, :exponential or :lundy-mees
bbvb :number-of-particles 100 Number of particles