normalise_logprob

Converts log-probabilities into a probability distribution
by exponentiating in a numerically stable way.

Implements a semi-supervised learning framework for finite mixture
models under a mixed-missingness mechanism. The approach models both
missing completely at random (MCAR) and entropy-based missing at random
(MAR) processes using a logistic–entropy formulation. Estimation is carried
out via an Expectation–-Conditional Maximisation (ECM) algorithm with robust
initialisation routines for stable convergence. The methodology relates to
the statistical perspective and informative missingness behaviour discussed
in Ahfock and McLachlan (2020) <doi:10.1007/s11222-020-09971-5> and
Ahfock and McLachlan (2023) <doi:10.1016/j.ecosta.2022.03.007>. The package
provides functions for data simulation, model estimation, prediction, and
theoretical Bayes error evaluation for analysing partially labelled data
under a mixed-missingness mechanism.

Jinran Wu

SSLfmm

Semi-Supervised Learning under a Mixed-Missingness Mechanism in
Finite Mixture Models

Geoffrey J. McLachlan

normalise_logprob function

<dl><dt>log_probs</dt>
<dd>A numeric vector of log-probabilities.</dd></dl>

Arguments

Normalise Log-Probabilities — normalise_logprob

<dl>

<dt>log_probs</dt>
<dd>A numeric vector of log-probabilities.</dd>

</dl>

normalise_logprob: Normalise Log-Probabilities

Description

Usage

Value

Arguments

Examples