Uniform

Defined in: distributions/uniform.js:7

Continuous uniform distribution on [lower, upper].

Extends

• Distribution

Constructors

Constructor

new Uniform(lower?, upper?, name?): Uniform

Defined in: distributions/uniform.js:15

Create a continuous uniform distribution on [lower, upper].

Parameters

lower?

any = 0

Lower bound, or an options object { lower | min, upper | max, name }

upper?

number | any[]

Upper bound

name?

string = 'Uniform'

Name of the distribution

Returns

Uniform

Overrides

Distribution.constructor

Properties

_dist

_dist: any

Defined in: distributions/uniform.js:25

---

lower

lower: any

Defined in: distributions/uniform.js:23

---

name

name: any

Defined in: distributions/uniform.js:19

Inherited from

Distribution.name

---

observed

observed: any

Defined in: distributions/base.js:47

Inherited from

Distribution.observed

---

upper

upper: number | any[]

Defined in: distributions/uniform.js:24

Methods

_len()

_len(value): number

Defined in: distributions/base.js:63

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

Parameters

value

number | any[]

Value(s) whose length participates in broadcasting

Returns

number

The broadcast length (0 when every input is scalar)

Inherited from

Distribution._len

---

_params()

_params(): object

Defined in: distributions/uniform.js:32

The proba parameter object for this distribution (proba {low, high} keys).

Returns

object

high

high: number | any[]

low

low: number | any[]

Overrides

Distribution._params

---

_paramsAt()

_paramsAt(i): any

Defined in: distributions/base.js:76

The proba parameter object with each array parameter indexed at i.

Parameters

i

number

Broadcast index

Returns

any

Per-element parameter object (scalars passed through)

Inherited from

Distribution._paramsAt

---

cdf()

cdf(value): number

Defined in: distributions/base.js:162

Cumulative distribution function (scalar parameters).

Parameters

value

number

Returns

number

Inherited from

Distribution.cdf

---

dlogProbDx()

dlogProbDx(value): number | number[]

Defined in: distributions/base.js:132

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: distributions/uniform.js:40

Get the distribution’s parameters.

Returns

object

lower

lower: number | any[]

upper

upper: number | any[]

Overrides

Distribution.getParams

---

logpdf()

logpdf(value): number | number[]

Defined in: distributions/base.js:120

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: distributions/base.js:91

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: distributions/base.js:203

Get the mean of the distribution

Returns

number | number[]

The mean

Inherited from

Distribution.mean

---

observe()

observe(data): Distribution

Defined in: distributions/base.js:194

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: distributions/base.js:152

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: distributions/base.js:171

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: distributions/base.js:183

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: distributions/base.js:213

Get the variance of the distribution

Returns

number | number[]

The variance

Inherited from

Distribution.variance