LikGauss: Gaussian Likelihood

In duckmayr/gpmss: Gaussian Process Models for Social Science

Description

The likelihood of y_i given f_i is normal with mean f_i and variance σ_y^2. It takes a single hyperparameter, log(σ_y).

Warning

Note the hyperparameter should be given on the log scale!

Note

The f_derivative() method is currently unimplemented for the Gaussian likelihood (it is not needed for parameter optimization).

Super class

gpmss::LikelihoodFunction -> LikGauss

Public fields

name

A character vector of length one giving the likelihood function's name; "Gaussian"

hypers

A numeric vector giving the likelihood function's hyperparameters

Methods

Public methods

• LikGauss$lp()

• LikGauss$f_derivative()

• LikGauss$new()

• LikGauss$clone()

Inherited methods

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Method lp()

Compute log probability of outcomes given function values

Usage

LikGauss$lp(y, f, hypers = NULL)

Arguments

y

The observed outcomes

f

The function values (generally a posterior mean or mode)

hypers

A numeric vector giving hyperparameters for the likelihood function. If NULL (the default), the hypers data member is used.

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Method f_derivative()

Compute partial derivatives of log likelihood function with respect to f

Usage

LikGauss$f_derivative(y, f, hypers = NULL, order = 1)

Arguments

y

The observed outcomes

f

The function values (generally a posterior mean or mode)

hypers

A numeric vector giving hyperparameters for the likelihood function. If NULL (the default), the hypers data member is used.

order

An integer vector of length one giving order of derivative desired; the default is 1

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Method new()

Create a new LikGauss object

Usage

LikGauss$new(hypers = 0)

Arguments

hypers

A numeric vector giving hyperparameters for the likelihood function; a numeric vector of length one giving the (log of) the standard deviation of the normal distribution for the likelihood

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Method clone()

The objects of this class are cloneable with this method.

Usage

LikGauss$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

duckmayr/gpmss documentation built on Nov. 8, 2021, 5:48 a.m.