A new inequality for maximum likelihood estimation in statistical models with latent variables

12/06/2019
by   Niels Lundtorp Olsen, et al.
0

Maximum-likelihood estimation (MLE) is arguably the most important tool for statisticians, and many methods have been developed to find the MLE. We present a new inequality involving posterior distributions of a latent variable that holds under very general conditions. It is related to the EM algorithm and has a clear potential for being used in a similar fashion.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/10/2021

Maximum Likelihood Estimation from a Tropical and a Bernstein–Sato Perspective

In this article, we investigate Maximum Likelihood Estimation with tools...
research
10/19/2012

Boltzmann Machine Learning with the Latent Maximum Entropy Principle

We present a new statistical learning paradigm for Boltzmann machines ba...
research
07/13/2021

Graphical Laplace-approximated maximum likelihood estimation: approximated likelihood inference for network data analysis

We derive Laplace-approximated maximum likelihood estimators (GLAMLEs) o...
research
12/29/2017

Finite-sample risk bounds for maximum likelihood estimation with arbitrary penalties

The MDL two-part coding index of resolvability provides a finite-sampl...
research
06/02/2023

A Stochastic Multivariate Latent Variable Model For Categorical Responses

This paper introduces a mathematical framework of a stochastic process m...
research
10/22/2019

Better Approximate Inference for Partial Likelihood Models with a Latent Structure

Temporal Point Processes (TPP) with partial likelihoods involving a late...
research
06/05/2023

Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models

Latent Gaussian models have a rich history in statistics and machine lea...

Please sign up or login with your details

Forgot password? Click here to reset