Distribution

Defined in: mc/src/distributions/base.js:40

Base class for probability distributions.

Subclasses set this._dist (a @tangent.to/proba distribution) in their constructor and implement _params() returning the proba parameter object (fields may be numbers or arrays of numbers).

Extended by

• Normal
• Uniform
• Bernoulli
• Beta
• Gamma
• Lognormal
• HalfNormal

Constructors

Constructor

new Distribution(name?): Distribution

Defined in: mc/src/distributions/base.js:44

Parameters

name?

string = 'Distribution'

Name of the distribution

Returns

Distribution

Properties

name

name: string

Defined in: mc/src/distributions/base.js:45

---

observed

observed: any

Defined in: mc/src/distributions/base.js:46

Methods

_len()

_len(value): number

Defined in: mc/src/distributions/base.js:55

Broadcast length across value and parameters (0 = all scalar).

Parameters

value

any

Returns

number

---

_params()

_params(): any

Defined in: mc/src/distributions/base.js:50

Returns

any

proba parameter object; subclasses must implement

---

_paramsAt()

_paramsAt(i): object

Defined in: mc/src/distributions/base.js:63

Parameters

i

any

Returns

object

---

cdf()

cdf(value): number

Defined in: mc/src/distributions/base.js:149

Cumulative distribution function (scalar parameters).

Parameters

value

number

Returns

number

---

dlogProbDx()

dlogProbDx(value): number | number[]

Defined in: mc/src/distributions/base.js:119

Derivative of logProb with respect to the value, elementwise. Used by Model.logProbAndGradient for analytic prior gradients. Discrete distributions return 0 (no dx in their gradient contract).

Parameters

value

number | any[]

Value(s) at which to differentiate

Returns

number | number[]

---

getParams()

getParams(): any

Defined in: mc/src/distributions/base.js:211

Get the distribution’s parameters as a plain object. Subclasses override to expose their specific parameters.

Returns

any

Parameters

---

logpdf()

logpdf(value): number | number[]

Defined in: mc/src/distributions/base.js:107

Alias for Distribution#logProb, matching the @tangent.to/proba distribution contract (which names the method logpdf). Lets code written against proba’s distributions work unchanged on mc’s.

Parameters

value

any

Value(s) to evaluate

Returns

number | number[]

---

logProb()

logProb(value): number | number[]

Defined in: mc/src/distributions/base.js:78

Log probability density/mass function. Broadcasts over array values and/or array parameters.

Parameters

value

any

Value(s) to evaluate

Returns

number | number[]

Log probability, elementwise for arrays

---

mean()

mean(): number | number[]

Defined in: mc/src/distributions/base.js:190

Get the mean of the distribution

Returns

number | number[]

The mean

---

observe()

observe(data): Distribution

Defined in: mc/src/distributions/base.js:181

Set observed data for this distribution

Parameters

data

number | any[]

Observed data

Returns

Distribution

this, for chaining

---

pdf()

pdf(value): number | number[]

Defined in: mc/src/distributions/base.js:139

Probability density/mass function, exp(logProb(value)).

Parameters

value

number | any[]

Value(s) to evaluate

Returns

number | number[]

---

quantile()

quantile(p): number

Defined in: mc/src/distributions/base.js:158

Quantile (inverse cdf) function (scalar parameters).

Parameters

p

number

Probability in [0, 1]

Returns

number

---

sample()

sample(shape?): number | number[]

Defined in: mc/src/distributions/base.js:170

Sample from the distribution using the package RNG (see setRandomSeed). sample() / sample([]) return a number; sample(n) / sample([n]) return an Array of n draws.

Parameters

shape?

number | number[]

Number of samples

Returns

number | number[]

---

variance()

variance(): number | number[]

Defined in: mc/src/distributions/base.js:200

Get the variance of the distribution

Returns

number | number[]

The variance