Methods¶
Enumeration¶

Infer
({model: ..., method: 'enumerate'[, ...]})¶ This method performs inference by enumeration.
The following options are supported:

maxExecutions
Maximum number of (complete) executions to enumerate.
Default:
Infinity

strategy
The traversal strategy used to explore executions. Either
'likelyFirst'
,'depthFirst'
or'breadthFirst'
.Default:
'likelyFirst'
ifmaxExecutions
is finite,'depthFirst'
otherwise.
Example usage:
Infer({method: 'enumerate', maxExecutions: 10, model: model}); Infer({method: 'enumerate', strategy: 'breadthFirst', model: model});

Rejection sampling¶

Infer
({model: ..., method: 'rejection'[, ...]}) This method performs inference using rejection sampling.
The following options are supported:

samples
The number of samples to take.
Default:
100

maxScore
An upper bound on the total factor score perexecution.
Default:
0

incremental
Enable incremental mode.
Default:
false
Incremental mode improves efficiency by rejecting samples before execution reaches the end of the program where possible. This requires every call to
factor(score)
in the program (across all possible executions) to havescore <= 0
.Example usage:
Infer({method: 'rejection', samples: 100, model: model});

MCMC¶

Infer
({model: ..., method: 'MCMC'[, ...]}) This method performs inference using Markov chain Monte Carlo.
The following options are supported:

samples
The number of samples to take.
Default:
100

lag
The number of additional iterations to perform between samples.
Default:
0

burn
The number of additional iterations to perform before collecting samples.
Default:
0

kernel
The transition kernel to use for inference. See Kernels.
Default:
'MH'

verbose
When
true
, print the current iteration and acceptance ratio to the console during inference.Default:
false

onlyMAP
When
true
, only the sample with the highest score is retained. The marginal is a delta distribution on this value.Default:
false
Example usage:
Infer({method: 'MCMC', samples: 1000, lag: 100, burn: 5, model: model});

Kernels¶
The following kernels are available:

MH
Implements single site MetropolisHastings. [wingate11]
This kernel makes use of any drift kernels specified in the model.
Example usage:
Infer({method: 'MCMC', kernel: 'MH', model: model});

HMC
Implements Hamiltonian Monte Carlo. [neal11]
As the HMC algorithm is only applicable to continuous variables,
HMC
is a cycle kernel which includes a MH step for discrete variables.The following options are supported:

steps
The number of steps to take periteration.
Default:
5

stepSize
The size of each step.
Default:
0.1

Example usage:
Infer({method: 'MCMC', kernel: 'HMC', model: model});
Infer({method: 'MCMC', kernel: {HMC: {steps: 10, stepSize: 1}}, model: model});
Incremental MH¶

Infer
({model: ..., method: 'incrementalMH'[, ...]}) This method performs inference using C3. [ritchie15]
This method makes use of any drift kernels specified in the model.
The following options are supported:

samples
The number of samples to take.
Default:
100

lag
The number of additional iterations to perform between samples.
Default:
0

burn
The number of additional iterations to perform before collecting samples.
Default:
0

verbose
When
true
, print the current iteration to the console during inference.Default:
false

onlyMAP
When
true
, only the sample with the highest score is retained. The marginal is a delta distribution on this value.Default:
false
Example usage:
Infer({method: 'incrementalMH', samples: 100, lag: 5, burn: 10, model: model});
To maximize efficiency when inferring marginals over multiple variables, use the
query
table, rather than building up a list of variable values:var model = function() { var hmm = function(n, obs) { if (n === 0) return true; else { var prev = hmm(n1, obs); var state = transition(prev); observation(state, obs[n]); query.add(n, state); return state; } }; hmm(100, observed_data); return query; } Infer({method: 'incrementalMH', samples: 100, lag: 5, burn: 10, model: model});
query
is a writeonly table which can be returned from a program (and thus marginalized). The only operation it supports is adding named values:
SMC¶

Infer
({model: ..., method: 'SMC'[, ...]}) This method performs inference using sequential Monte Carlo. When
rejuvSteps
is 0, this method is also known as a particle filter.The following options are supported:

particles
The number of particles to simulate.
Default:
100

rejuvSteps
The number of MCMC steps to apply to each particle at each
factor
statement. With this addition, this method is often called a particle filter with rejuvenation.Default:
0

rejuvKernel
The MCMC kernel to use for rejuvenation. See Kernels.
Default:
'MH'

importance
Controls the importance distribution used during inference.
Specifying an importance distribution can be useful when you know something about the posterior distribution, as specifying an importance distribution that is closer to the posterior than the prior will improve the statistical efficiency of inference.
This option accepts the following values:
'default'
: When a random choice has a guide distribution specified, use that as the importance distribution. For all other random choices, use the prior.'ignoreGuide'
: Use the prior as the importance distribution for all random choices.'autoGuide'
: When a random choice has a guide distribution specified, use that as the importance distribution. For all other random choices, use a default guide distribution as the importance distribution.Default:
'default'

onlyMAP
When
true
, only the sample with the highest score is retained. The marginal is a delta distribution on this value.Default:
false
Example usage:
Infer({method: 'SMC', particles: 100, rejuvSteps: 5, model: model});

Optimization¶

Infer
({model: ..., method: 'optimize'[, ...]}) This method performs inference by optimizing the parameters of the guide program. The marginal distribution is a histogram constructed from samples drawn from the guide program using the optimized parameters.
The following options are supported:

samples
The number of samples used to construct the marginal distribution.
Default:
100

onlyMAP
When
true
, only the sample with the highest score is retained. The marginal is a delta distribution on this value.Default:
false
In addition, all of the options supported by Optimize are also supported here.
Example usage:
Infer({method: 'optimize', samples: 100, steps: 100, model: model});

Forward Sampling¶

Infer
({model: ..., method: 'forward'[, ...]}) This method builds a histogram of return values obtained by repeatedly executing the program given by
model
, ignoring anyfactor
statements encountered while doing so. Sincecondition
andobserve
are written in terms offactor
, they are also effectively ignored.This means that unlike all other methods described here, forward sampling does not perform marginal inference. However, sampling from a model without any factors etc. taken into account is often useful in practice, and this method is provided as a convenient way to achieve that.
The following options are supported:

samples
The number of samples to take.
Default:
100

guide
When
true
, sample random choices from the guide. A default guide distribution is used for random choices that do not have a guide distribution specified explicitly.When
false
, sample from the model.Default:
false

onlyMAP
When
true
, only the sample with the highest score is retained. The marginal is a delta distribution on this value.Default:
false
Example usage:
Infer({method: 'forward', model: model}); Infer({method: 'forward', guide: true, model: model});

Bibliography
[wingate11]  David Wingate, Andreas Stuhlmüller, and Noah D. Goodman. “Lightweight implementations of probabilistic programming languages via transformational compilation.” International Conference on Artificial Intelligence and Statistics. 2011. 
[neal11]  Radford M. Neal, “MCMC using Hamiltonian dynamics.” Handbook of Markov Chain Monte Carlo 2 (2011). 
[ritchie15]  Daniel Ritchie, Andreas Stuhlmüller, and Noah D. Goodman. “C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching.” International Conference on Artificial Intelligence and Statistics. 2016. 