A Note on New Bernstein-type Inequalities for the Log-likelihood Function of Bernoulli Variables

08/31/2019
by   Yunpeng Zhao, et al.
0

We prove a new Bernstein-type inequality for the log-likelihood function of Bernoulli variables. In contrast to classical Bernstein's inequality and Hoeffding's inequality when applied to the log-likelihood, the new bound is independent of the parameters of the Bernoulli variables and therefore does not blow up as the parameters approach 0 or 1. The new inequality strengthens certain theoretical results on likelihood-based methods for community detection in networks and can be applied to other likelihood-based methods for binary data.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/09/2020

On Optimal Uniform Concentration Inequalities for Discrete Entropies in the High-dimensional Setting

We prove an exponential decay concentration inequality to bound the tail...
research
10/08/2015

Empirical Analysis of Sampling Based Estimators for Evaluating RBMs

The Restricted Boltzmann Machines (RBM) can be used either as classifier...
research
02/23/2018

Accelerate iterated filtering

In simulation-based inferences for partially observed Markov process mod...
research
03/09/2022

Concave likelihood-based regression with finite-support response variables

We propose likelihood-based methods for regression when the response var...
research
10/16/2015

Change Detection in Multivariate Datastreams: Likelihood and Detectability Loss

We address the problem of detecting changes in multivariate datastreams,...
research
08/18/2016

Parameter Learning for Log-supermodular Distributions

We consider log-supermodular models on binary variables, which are proba...
research
11/28/2019

A note on the Lomax distribution

The Lomax distribution is a popularly used heavy-tailed distribution tha...

Please sign up or login with your details

Forgot password? Click here to reset