On a notion of partially conditionally identically distributed sequences

TL;DR

This paper introduces a new concept of partial conditional identical distribution for families of sequences, extending the idea of c.i.d. sequences to more complex dependence structures, and establishes their asymptotic properties relevant to Bayesian statistics.

Contribution

It extends the notion of c.i.d. sequences to families, defines partial c.i.d. dependence, and proves limit theorems showing their asymptotic behavior and relevance to Bayesian prediction.

Findings

Partially c.i.d. families are asymptotically partially exchangeable.

Strong laws of large numbers hold for these families.

Central limit theorems are established for empirical means.

Abstract

A notion of conditionally identically distributed (c.i.d.) sequences has been studied as a form of stochastic dependence that is weaker than exchangeability, but is equivalent to exchangeability for stationary sequences. In this article we extend this notion to families of sequences. Paralleling the extension from exchangeability to partial exchangeability in the sense of de Finetti, we propose a notion of partially c.i.d. dependence, that is equivalent to partial exchangeability for stationary processes. Partially c.i.d. families of sequences preserve attractive limit properties of partial exchangeability, and are asymptotically partially exchangeable. Moreover, we provide strong laws of large numbers and two central limit theorems. Our focus is on the asymptotic agreement of predictions and empirical means, which lies in the foundations of Bayesian statistics. Natural examples are interacting randomly reinforced processes satisfying certain conditions on the reinforcement.