Maximum Likelihood Parameters (Maximum Likelihood Parameters)
Jump to navigation
Jump to search
Description
In these algorithms, the goal is to estimate hyperparameters using maximum likelihood.
Parameters
$n$: number of observations in sample
$r$: number of parameters + latent variables
Table of Algorithms
Name | Year | Time | Space | Approximation Factor | Model | Reference |
---|---|---|---|---|---|---|
Expectation–maximization (EM) algorithm | 1977 | $O(n^{3})$ | $O(n+r)$? | Exact | Deterministic | Time |
Newton–Raphson algorithm | 1685 | $O(n^{3})$ | $O(n+r^{2})$? | Exact | Deterministic | |
Parameter-expanded expectation maximization (PX-EM) algorithm | 1998 | $O(n^{3})$ | $O(n+r)$? | Exact | Deterministic | Time |
Expectation conditional maximization (ECM) | 2017 | $O(n^{2} \log n)$ | $O(n+r)$? | Exact | Deterministic | Time |
Generalized expectation maximization (GEM) algorithm | 1994 | $O(n^{4} \log^{0.1}(.{5}n)$) | $O(n+r)$? | Exact | Deterministic | Time |
α-EM algorithm | 2003 | $O(n^{3})$ | $O(n+r)$? | Exact | Deterministic | Time |