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Activity Number: 297 - SBSS Student Travel Award Session 1
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #329639
Title: Recursive Non-Parametric Predictive for a Discrete Regression Model
Author(s): Lorenzo Cappello* and Stephen Walker
Companies: and
Keywords: Recursive Algorithm; Density regression; Bayesian nonparametrics

We propose a recursive algorithm to estimate a finite set of conditional distributions. The procedure is fully nonparametric and has a Bayesian interpretation. Indeed, the recursive process follows a certain Bayesian update. We prove weak convergence of the sequence of distributions, using a fixed point argument. This asymptotic result is new in the context of recursive algorithms. We demonstrate numerical accuracy via simulations. The estimate is very fast, it is sequential and requires limited computing power; being also parallelizable.

Authors who are presenting talks have a * after their name.

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