Model

Defined in: model.js:42

Bayesian probabilistic model: a DAG of random variables (priors), observed likelihoods, generic Model#potential log-density terms, and named Model#deterministic transforms, exposing the joint log-probability and its gradient for the MCMC samplers.

Constructors

Constructor

new Model(name?): Model

Defined in: model.js:53

Accepts either a positional name or a single options object { name }.

Parameters

name?

any = 'model'

Model name, or an options object { name }

Returns

Model

Examples

new Model('linear_regression')

new Model({ name: 'linear_regression' })

Properties

deterministics

deterministics: Map<any, any>

Defined in: model.js:62

---

name

name: any

Defined in: model.js:57

---

observedVars

observedVars: Map<any, any>

Defined in: model.js:59

---

potentialGrads

potentialGrads: Map<any, any>

Defined in: model.js:61

---

potentials

potentials: Map<any, any>

Defined in: model.js:60

---

variables

variables: Map<any, any>

Defined in: model.js:58

Methods

_potentialSum()

_potentialSum(params): number

Defined in: model.js:151

Sum of all potential terms at the given parameter values.

Parameters

params

any

Returns

number

---

addVariable()

addVariable(name, distribution, observed?): Distribution

Defined in: model.js:130

Add a random variable to the model

Parameters

name

string

Name of the variable

distribution

Distribution

Distribution of the variable

observed?

any = null

Observed data (optional)

Returns

Distribution

The distribution

---

computeDeterministics()

computeDeterministics(trace): any

Defined in: model.js:363

Evaluate registered Model#deterministic transforms on each posterior draw and append them to the trace as extra columns. Computed post-hoc - they do not affect sampling - and the MCMC samplers call this automatically before returning their trace. Each fn(params) receives a {name: number} map of the free-variable values for one draw and may return a number or an array (legacy tensor-like returns with arraySync are read out too).

Parameters

trace

any

Trace map { name: [...] } or a { trace } wrapper.

Returns

any

The same trace, with one column per deterministic.

---

deterministic()

deterministic(name, fn): Model

Defined in: model.js:118

Register a named deterministic transform of the parameters for recording in the trace (computed post-hoc from posterior draws). Deterministics do NOT affect the log-probability - use Model#potential for likelihood or factor terms.

Parameters

name

string

Identifier for the transform

fn

(params) => number | any[]

The transform

Returns

Model

this

---

getFreeVariableNames()

getFreeVariableNames(): string[]

Defined in: model.js:342

Get list of unobserved variable names

Returns

string[]

Variable names

---

getVariable()

getVariable(name): Distribution

Defined in: model.js:146

Get a variable from the model

Parameters

name

string

Name of the variable

Returns

Distribution

The distribution

---

gradientsOnly()

gradientsOnly(params): any

Defined in: model.js:237

Gradient of the joint log-probability WITHOUT its value — exactly logProbAndGradient(params).gradients, skipping the potential-value pass.

Samplers’ leapfrog steps only consume the gradient, but for a model with an analytic potential gradient, computing the discarded value costs a full extra pass over the data at every leapfrog step. This method omits it; the returned gradients are identical.

Parameters

params

any

Parameter values as {name: number|Array} pairs

Returns

any

{name: number|Array} map of gradients

---

logProb()

logProb(params): number

Defined in: model.js:164

Compute the log probability of the model given parameter values

Parameters

params

any

Parameter values as {name: number|Array} pairs

Returns

number

Log probability (scalar)

---

logProbAndGradient()

logProbAndGradient(params): object

Defined in: model.js:198

Compute the log probability and its gradient with respect to parameters.

Prior terms are differentiated analytically (proba dlogpdf); potential terms by central finite differences with step h = 1e-6 * max(1, |x|) per scalar component.

Parameters

params

any

Parameter values as {name: number|Array} pairs

Returns

object

The scalar log probability and a {name: number|Array} map of gradients, one per parameter

gradients

gradients: any

logProb

logProb: number

---

potential()

potential(name, fn, gradFn?): Model

Defined in: model.js:101

Register a generic log-density term (a “potential” / factor) contributing to the joint log-probability. fn(params) receives the current free-variable values as plain numbers (or arrays) keyed by name and must return a number or an array of log-density values (which are summed into the total).

This is the general mechanism for likelihoods whose parameters are arbitrary deterministic functions of the latent variables and data - the deterministic expression is computed inside fn with ordinary JavaScript math:

model.potential('y', (v) =>
  new Normal(xData.map((x) => v.slope * x + v.intercept), v.sigma).logProb(yData));

Gradients of potentials are estimated by central finite differences by default; priors added with Model#addVariable get analytic gradients. For a large data term this finite-difference cost (2·(#free params) extra evaluations of fn per gradient) dominates NUTS/HMC. Pass an optional gradFn returning the analytic gradient of this term to avoid it entirely:

model.potential('y', (v) => new Normal(mu(v), v.sigma).logProb(yData),
  (v) => ({ slope: dSlope(v), intercept: dIntercept(v), sigma: dSigma(v) }));

gradFn(params) must return an object mapping each free-variable name to the partial derivative of THIS term’s log-density with respect to it (a number, or an array for a vector-valued variable). It is added to the analytic prior gradients; omit an entry whose partial is zero.

Parameters

name

string

Identifier for the term

fn

(params) => number | number[]

Returns log-density value(s)

gradFn?

(params) => any

Optional analytic gradient of fn

Returns

Model

this

---

predictPosterior()

predictPosterior(trace, predictFn, nSamples?): any[]

Defined in: model.js:395

Posterior predictive sampling Generate predictions by sampling from the posterior

Parameters

trace

any

Trace object from MCMC sampling

predictFn

Function

Function that takes params and returns predictions

nSamples?

number = null

Number of posterior samples to use (null = use all)

Returns

any[]

Array of predictions from each posterior sample

---

predictPosteriorSummary()

predictPosteriorSummary(trace, predictFn, credibleInterval?): any

Defined in: model.js:424

Compute posterior predictive mean and credible intervals

Parameters

trace

any

Trace object from MCMC sampling

predictFn

Function

Function that takes params and returns predictions

credibleInterval?

number = 0.95

Credible interval (e.g., 0.95 for 95%)

Returns

any

{mean, lower, upper} predictions

---

samplePrior()

samplePrior(nSamples?): any

Defined in: model.js:326

Sample from the prior distributions

Parameters

nSamples?

number = 1

Number of samples to generate

Returns

any

Samples as {name: Array} pairs

---

summary()

summary(): string

Defined in: model.js:473

Create a summary of the model

Returns

string

Model summary