Coming from Stan, PyMC, Turing, Gen, Anglican or WebPPL

A translation of concepts, and for each system what foerster does not have yet. Throughout, infer is org.replikativ.foerster.core, m foerster.measure, diagnostics foerster.diagnostics, and a model is a function returning a spindel spin (language).

Three differences hold for all of them:

• A model is a program run, not a graph. Sites are named with :id and reached by ordinary control flow. There is no vectorized ~ over arrays: observations are sites in a loop/recur (:id [:y i]), and effects inside closures passed to map or for are not seen by the spin macro (the rules).
• Inference returns a spin resolving an empirical measure — weighted particles, each with its value and trace (posteriors). At the REPL deref it inside sp/with-context; inside a spin await it.
• No gradients are derived from the model. HMC runs on block sites whose log density and gradient you supply (or compile with foerster-raster); everything else is gradient-free (random-walk and single-site MH, the particle methods, BBVI with score-function gradients).

Stan

Stan	foerster
parameters { real mu; } and mu ~ normal(0, 1);	(sample (dist/normal 0.0 1.0) :id :mu)
real<lower=0> sigma; with sigma ~ normal(0, 1);	(sample (dist/half-normal 1.0) :id :sigma): the support comes from the distribution
y ~ normal(mu, sigma); (data)	(observe (dist/normal mu sigma) y :id :y)
target += lp;	(factor lp)
transformed parameters, generated quantities	ordinary let bindings; (deterministic v :id :a) records a value in the trace
log_lik in generated quantities	(diagnostics/pointwise-log-likelihood measure)
y_rep in generated quantities	(infer/predictive model measure n)
sample(chains=4) (NUTS)	(infer/infer model {:method :rmh :iterations … :chains 4 :burn …}), or HMC on a block (k/hmc-kernel)
R-hat, ESS, MCSE in the summary	(diagnostics/summary measure :mu) — the same rank-normalized split R-hat and bulk/tail ESS
bridge sampling (evidence)	(m/log-marginal measure) of an SMC or importance-sampling run
ADVI	{:method :bbvi}: mean-field, score-function gradients

Not in foerster yet:

• NUTS, step-size and mass-matrix adaptation, and gradients derived from the model. HMC needs a hand-written or raster-compiled gradient, a fixed step size and number of steps, and runs on unconstrained real block latents only: no automatic transforms of constrained parameters.
• Optimization (optimize, MAP estimates) and Laplace approximations.
• Stan's speed on the models it is built for. A model with all observations at the end and smooth continuous latents fits in Stan in seconds; in foerster it takes a random walk or a hand-supplied gradient.

PyMC

PyMC	foerster
with pm.Model():	(defn model [data] (spin …))
pm.Normal("mu", 0, 1)	(sample (dist/normal 0.0 1.0) :id :mu)
pm.Normal("y", mu, sigma, observed=y)	(observe (dist/normal mu sigma) y :id :y), one per datum
pm.Potential("p", lp)	(factor lp)
pm.Deterministic("d", x)	(deterministic x :id :d)
pm.sample(chains=4)	(infer/infer model {:method :rmh :chains 4 …})
pm.sample_smc()	{:method :tempered} — also tempered SMC; or {:method :smc} in program order
pm.sample_prior_predictive()	(gfi/run-policy model (trace/policy {:simulate-observed? true})) (workflow)
pm.sample_posterior_predictive(idata)	(infer/predictive model measure n)
az.summary, az.rhat, az.ess	diagnostics/summary, rhat, ess-bulk, ess-tail, mcse
pm.compute_log_likelihood, az.loo	diagnostics/pointwise-log-likelihood, then ArviZ or R's loo
pm.do(model, {"x": 1})	{:policy (trace/policy {:interventions {:x {:do 1}}})} on a particle method

Not in foerster yet: NUTS and automatic gradients (as for Stan), automatic transforms, ADVI with reparameterization gradients, Gaussian-process and other random-process building blocks, and vectorized distributions over arrays. PSIS-LOO and WAIC are computed outside foerster.

Turing

Turing	foerster
@model function f(y) … end	(defn f [y] (spin …))
mu ~ Normal(0, 1)	(sample (dist/normal 0.0 1.0) :id :mu)
y ~ Normal(mu, 1) with y an argument	(observe (dist/normal mu 1.0) y :id :y)
@addlogprob! lp	(factor lp)
model \| (; mu = 0.3)	the particle methods' :policy (trace/policy {:constraints {:mu 0.3}}), or gfi/generate
sample(m, MH(), n)	{:method :mh :iterations n}
sample(m, SMC(), n), PG(n), IS()	{:method :smc}, {:method :pgibbs}, {:method :importance}
Gibbs(:a => HMC(…), :b => PG(…))	k/block-gibbs-kernel, or kernels composed by k/cycle / k/mixture (algorithms)
MCMCThreads(), 1000, 4	:chains 4
predict(m_missing, chain)	(infer/predictive model measure n)
generated_quantities	deterministic sites, or infer/query with a function of the value
pointwise_loglikelihoods	diagnostics/pointwise-log-likelihood
MCMCChains summary	diagnostics/summary

Not in foerster yet: NUTS and automatic differentiation, automatic transforms (Bijectors), particle Gibbs as a component of a Gibbs sampler (k/cycle and k/mixture compose Markov-chain kernels only: MH, block Gibbs and HMC), and MAP/MLE optimization. Conditioning from outside (|) works for the particle methods and the generative function interface; the Markov-chain methods take no :policy, so write the observation into the model.

Gen

foerster implements Gen's generative function interface over its traces (foerster.gfi). Each operation is a CPS operation: await it in a spin.

Gen	foerster
@gen function f() … end	(defn f [] (spin …))
{:x} ~ normal(0, 1)	(sample (dist/normal 0.0 1.0) :id :x)
{:x => i => :y} ~ …	:id [:x i :y], or with-scope (addresses)
{:sub} ~ g()	(await (g)) inside with-scope [:sub]
choicemap((:x, 1.0))	a map {:x 1.0}
simulate(f, args)	(gfi/simulate (f))
generate(f, args, constraints)	(gfi/generate (f) constraints) → {:trace :weight}
assess(f, args, choices)	(gfi/assess (f) choices)
update(tr, args, argdiffs, constraints)	(gfi/update tr constraints) → {:trace :weight :discard}
regenerate(tr, args, argdiffs, sel)	(gfi/regenerate tr selection)
select(:x)	#{:x}, or a spindel selector (select/id, select/path, select/prefix)
mh(tr, sel)	(gfi/mh tr selection) → {:trace :accepted?}
mh(tr, proposal, args, involution)	(involutive/step tr {:propose :log-q :involution})
get_choices, get_retval, get_score	trace/choices, (:trace/result t), trace/log-joint
importance_sampling, particle_filter_*	infer/importance-sampling, infer/smc-infer, smc/stream
SMCP3	smc-infer with :anchors and :smcp3

Traces hold worlds: give them back with gfi/close!, and after update or regenerate release the one you do not keep (spindel.trace/release!).

Not in foerster yet:

• Arguments and argdiffs. A model's arguments are closed over when its spin is built; update and regenerate cannot change them, and there are no argdiffs for incremental re-execution.
• Combinators (Map, Unfold, Recurse, Switch) and the static modeling language. Replay restarts from the earliest changed site.
• mh with a custom proposal program (mh(tr, proposal, args)): gfi/mh regenerates from the prior; a custom move is an involutive move, whose Jacobian the caller supplies (involutive/fd-log-jacobian computes it numerically). Moves that add or remove sites (reversible jump) are not supported.
• Trainable parameters (@param, train!) and amortized training. foerster.learn exports trajectories as training data; training happens elsewhere.
• propose, project, trace translators, and enumeration.

Anglican

foerster keeps Anglican's design — sample and observe as checkpoints of a continuation-passing program, inference as a handler — and most of its algorithms.

Anglican	foerster
(defquery q [data] …)	(defn q [data] (spin …))
(defm f [x] …)	(defn f [x] (spin …)), called with (await (f x))
(sample (normal 0 1))	(sample (dist/normal 0.0 1.0) :id :mu) — name sites you will read
(observe (normal mu 1) y)	(observe (dist/normal mu 1.0) y)
(predict :mu mu)	the program's return value, or (deterministic mu :id :mu)
(store …), (retrieve …)	state in the particle's world (spindel atoms and signals fork with it)
(doquery :smc q [data] :number-of-particles 1000)	(infer/infer (q data) {:method :smc :particles 1000})
:importance, :smc, :pimh, :pgibbs, :pgas, :ipmcmc, :bbvi	the same keywords for infer/infer
:lmh	{:method :mh}
:pcascade	(cascade/cascade model n opts)
(gamma shape rate)	(dist/gamma shape scale): scale, not rate
with-primitive-procedures	not needed: a model calls any Clojure function

doquery returns a lazy, unbounded sequence of samples; infer/infer resolves one finite measure.

Not in foerster yet:

• mem, and the random processes built on it (CRP, Dirichlet process, Gaussian process).
• Nested inference: conditional and queries used as distributions.
• Higher-order functions with sites inside them. Anglican transformed map and reduce inside a query; in foerster an observe inside a closure passed to map is not seen by the macro — use loop/recur.
• :almh (adaptive site choice), and an anytime lazy stream of samples.

WebPPL

WebPPL	foerster
var model = function() { … }	(defn model [] (spin …))
sample(Gaussian({mu: 0, sigma: 1}))	(sample (dist/normal 0.0 1.0) :id :mu)
observe(Gaussian({mu, sigma: 1}), y)	(observe (dist/normal mu 1.0) y)
factor(s)	(factor s)
condition(b)	(factor (if b 0.0 ##-Inf))
Infer({method: 'SMC', particles: 1000, model})	(infer/infer (model) {:method :smc :particles 1000})
Infer({method: 'MCMC', kernel: 'MH'})	{:method :mh}
Infer({method: 'forward'})	gfi/simulate, or {:method :importance} on a model without data
expectation(d, f)	(:mean (infer/query measure f))
d.normalizationConstant	(m/log-marginal measure)

WebPPL's sources include an asynchronous particle filter and particle MCMC, prior art for foerster's cascade and particle-MCMC methods (literature).

Not in foerster yet: exact enumeration ('enumerate'), rejection sampling, incremental MH, mem, nested Infer (a posterior used as a distribution inside another model), and variational inference with guide programs and trainable parameters ('optimize'); foerster's BBVI learns a mean-field guide only.

What foerster has that these do not all have

• Particles that run in forks of a world with side effects (:world-policy :fork; worlds).
• Particle MCMC (PIMH, particle Gibbs, PGAS, IPMCMC), PMMH and SMC² over general programs, with evidence estimates from every SMC method.
• SMC without barriers: arrival-batched SMC and the particle cascade.
• Streaming SMC with a population that can be forked.
• Interventions and twin-world counterfactuals (counterfactuals notebook).
• Steering a process that has no density by SMC over scored steps, with the trajectories as training data.
• The same draws for a seed on the JVM and in JavaScript.