Documentation
¶
Overview ¶
Package dist provides differentiatable distribution models. The package is automatically differentiated by deriv during build.
Index ¶
Constants ¶
This section is empty.
Variables ¶
var Bernoulli bernoulli
var Beta beta
Beta distribution, singleton instance
var Binomial binomial
var Cauchy cauchy
Cauchy distribution, singleton instance
var D d
D is a singletone variable of type d. General log-likelihood handling functions are dispatched on d.
var Exponential, Expon exponential
Exponential distribution, singleton instance (Expon is kept for backward compatibility)
var Gamma gamma
Gamma distribution, singleton instance
var Normal normal
Normal distribution, singleton instance
Functions ¶
This section is empty.
Types ¶
type Categorical ¶ added in v0.5.1
type Categorical struct {
N int // number of categories
}
Categorical distribution
var Cat Categorical
Categorical distribution, singleton instance; Observe cannot be called on this instance, but Logp and Logps can.
func (Categorical) LogZ ¶ added in v1.0.7
func (dist Categorical) LogZ(alpha []float64) float64
LogZ computes the normalization constant.
func (Categorical) Logp ¶ added in v0.5.1
func (dist Categorical) Logp( alpha []float64, y float64, ) float64
Logp computes log pmf of a single observation.
func (Categorical) Logps ¶ added in v0.5.1
func (dist Categorical) Logps( alpha []float64, y ...float64, ) float64
Logps computes log pmf of a vector of observations.
func (Categorical) Observe ¶ added in v0.5.1
func (dist Categorical) Observe(x []float64) float64
Observe implements the Model interface
type Dirichlet ¶
type Dirichlet struct {
N int // number of dimensions
}
Dirichlet distribution
var Dir Dirichlet
Dirichlet distribution, singleton instance; Observe cannot be called on this instance, but Logp and Logps can.
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