• options (object) – Optimization options.


Optimizes the parameters of the guide program specified by the model option.

The following options are supported:


A function of zero arguments that specifies the target and guide programs.

This option must be present.


The number of optimization steps to take.

Default: 1


The optimization method used. The following methods are available:

  • 'sgd'
  • 'adagrad'
  • 'rmsprop'
  • 'adam'

Each method takes a stepSize sub-option, see below for example usage. Additional method specific options are available, see the adnn optimization module for details.

Default: 'adam'


Specifies the optimization objective and the method used to estimate its gradients. See Estimators.

Default: ELBO


Specifies the strength of an L2 penalty applied to all parameters during optimization.

More specifically, a term 0.5 * strength * paramVal^2 is added to the objective for each parameter encountered during optimization. Note that this addition is not reflected in the value of the objective reported during optimization.

For parameters of the model, when the objective is the ELBO, this is equivalent to specifying a mean zero and variance 1/strength Gaussian prior and a Delta guide for each parameter.

Default: 0


Specifies a function that will be called after each step. The function will be passed the index of the current step and the value of the objective as arguments. For example:

var callback = function(index, value) { /* ... */ };
Optimize({model: model, steps: 100, onStep: callback});

Default: true

Example usage:

Optimize({model: model, steps: 100});
Optimize({model: model, optMethod: 'adagrad'});
Optimize({model: model, optMethod: {sgd: {stepSize: 0.5}}});


The following estimators are available:


This is the evidence lower bound (ELBO). Optimizing this objective yields variational inference.

For best performance use mapData() in place of map() where possible when optimizing this objective. The conditional independence information this provides is used to reduce the variance of gradient estimates which can significantly improve performance, particularly in the presence of discrete random choices. Data sub-sampling is also supported through the use of mapData().

The following options are supported:


The number of samples to take for each gradient estimate.

Default: 1


Enable the “average baseline removal” variance reduction strategy.

Default: true


The decay rate used in the exponential moving average used to estimate baselines.

Default: 0.9

Example usage:

Optimize({model: model, estimator: 'ELBO'});
Optimize({model: model, estimator: {ELBO: {samples: 10}}});