Beta

Defined in: mc/src/distributions/beta.js:7

Beta distribution on (0, 1).

Extends

• Distribution

Constructors

Constructor

new Beta(alpha?, beta?, name?): Beta

Defined in: mc/src/distributions/beta.js:14

Parameters

alpha?

any = 1

First shape, or an options object { alpha, beta, name }

beta?

number | any[]

Second shape

name?

string = 'Beta'

Name of the distribution

Returns

Beta

Overrides

Distribution.constructor

Properties

_dist

_dist: Readonly<{ kind: "continuous"; logpdf: (t, __namedParameters) => number; name: "beta"; params: string[]; cdf: number; dlogpdf: { dalpha: any; dbeta: any; dx: number; }; mean: number; pdf: number; quantile: any; sample: number; sampleN: any[]; support: number[]; validate: void; variance: number; }>

Defined in: mc/src/distributions/beta.js:24

---

alpha

alpha: any

Defined in: mc/src/distributions/beta.js:22

---

beta

beta: number | any[]

Defined in: mc/src/distributions/beta.js:23

---

name

name: any

Defined in: mc/src/distributions/beta.js:18

Inherited from

Distribution.name

---

observed

observed: any

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

Inherited from

Distribution.observed

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

Inherited from

Distribution._len

---

_params()

_params(): object

Defined in: mc/src/distributions/beta.js:27

Returns

object

proba parameter object; subclasses must implement

alpha

alpha: any

beta

beta: number | any[]

Overrides

Distribution._params

---

_paramsAt()

_paramsAt(i): object

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

Parameters

i

any

Returns

object

Inherited from

Distribution._paramsAt

---

cdf()

cdf(value): number

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

Cumulative distribution function (scalar parameters).

Parameters

value

number

Returns

number

Inherited from

Distribution.cdf

---

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[]

Inherited from

Distribution.dlogProbDx

---

getParams()

getParams(): object

Defined in: mc/src/distributions/beta.js:35

Get the distribution’s parameters.

Returns

object

alpha

alpha: number | any[]

beta

beta: number | any[]

Overrides

Distribution.getParams

---

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[]

Inherited from

Distribution.logpdf

---

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

Inherited from

Distribution.logProb

---

mean()

mean(): number | number[]

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

Get the mean of the distribution

Returns

number | number[]

The mean

Inherited from

Distribution.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

Inherited from

Distribution.observe

---

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[]

Inherited from

Distribution.pdf

---

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

Inherited from

Distribution.quantile

---

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[]

Inherited from

Distribution.sample

---

variance()

variance(): number | number[]

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

Get the variance of the distribution

Returns

number | number[]

The variance

Inherited from

Distribution.variance