DeepAI
Log In Sign Up

Learning Kolmogorov Models for Binary Random Variables

06/06/2018
by   Hadi Ghauch, et al.
0

We summarize our recent findings, where we proposed a framework for learning a Kolmogorov model, for a collection of binary random variables. More specifically, we derive conditions that link outcomes of specific random variables, and extract valuable relations from the data. We also propose an algorithm for computing the model and show its first-order optimality, despite the combinatorial nature of the learning problem. We apply the proposed algorithm to recommendation systems, although it is applicable to other scenarios. We believe that the work is a significant step toward interpretable machine learning.

READ FULL TEXT

page 6

page 8

12/08/2018

Generalization of the pairwise stochastic precedence order to the sequence of random variables

We discuss a new stochastic ordering for the sequence of independent ran...
03/02/2018

On some discrete random variables arising from recent study on statistical analysis of compressive sensing

The recent paper [27] provides a statistical analysis for efficient dete...
09/17/2022

Some stochastic comparison results for frailty and resilience models

Frailty and resilience models provide a way to introduce random effects ...
06/21/2022

Copula bounds for circular data

We propose the extension of Fréchet-Hoeffding copula bounds for circular...
03/15/2022

Comparing two samples through stochastic dominance: a graphical approach

Non-deterministic measurements are common in real-world scenarios: the p...
05/20/2020

Dependence on a collection of Poisson random variables

We propose two novel ways of introducing dependence among Poisson counts...
01/31/2018

Compressed Anomaly Detection with Multiple Mixed Observations

We consider a collection of independent random variables that are identi...