Maximum Likelihood Methods in Unknown Latent Variables (Maximum Likelihood Methods in Unknown Latent Variables)

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Description

In this problem, the goal is to compute maximum-likelihood estimates when the observations can be viewed as incomplete data.

Parameters

No parameters found.

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
EM with Quasi-Newton Methods (Jamshidian; Mortaza; Jennrich; Robert I.) 1997 $O(n^{2} log^{3} n)$ $O(n+r^{2})$? Exact Deterministic Time
Parameter-expanded expectation maximization (PX-EM) 1998 $O(n^{3})$ $O(n+r)$? Exact Deterministic Time
Expectation conditional maximization (ECM) 1993 $O(n^{3})$ $O(n+r)$? Exact Deterministic Time
Expectation conditional maximization either (ECME) (Liu; Chuanhai; Rubin; Donald B) 1994 $O(n^{3})$ $O(n+r)$? Exact Deterministic Time
α-EM Algorithm 2003 $O(n^{3})$ $O(n+r)$? Exact Deterministic Time
Shaban; Amirreza; Mehrdad; Farajtabar 2015 $O(n^{2} log^{2} n)$ $O(kd+d^{3})$?? Exact Deterministic Time
alpha-HMM (Matsuyama, Yasuo) 2011 $O(n^{2} log^{2} n)$ Exact Deterministic Time

Time Complexity Graph

Maximum Likelihood Methods in Unknown Latent Variables - Time.png

Space Complexity Graph

Maximum Likelihood Methods in Unknown Latent Variables - Space.png

Time-Space Tradeoff

Maximum Likelihood Methods in Unknown Latent Variables - Pareto Frontier.png