Kernel based Dirichlet sequences

Let $X=(X_1,X_2,\ldots)$ be a sequence of random variables with values in a standard space $(S,\mathcal{B})$. Suppose \begin{gather*} X_1\simν\quad\text{and}\quad P\bigl(X_{n+1}\in\cdot\mid X_1,\ldots,X_n\bigr)=\frac{θν(\cdot)+\sum_{i=1}^nK(X_i)(\cdot)}{n+θ}\quad\quad\text{a.s.} \end{gather*} where $θ>0$ is a constant, $ν$ a probability measure on $\mathcal{B}$, and $K$ a random probability measure on $\mathcal{B}$. Then, $X$ is exchangeable whenever $K$ is a regular conditional distribution for $ν$ given any sub-$σ$-field of $\mathcal{B}$. Under this assumption, $X$ enjoys all the main properties of classical Dirichlet sequences, including Sethuraman's representation, conjugacy property, and convergence in total variation of predictive distributions. If $μ$ is the weak limit of the empirical measures, conditions for $μ$ to be a.s. discrete, or a.s. non-atomic, or $μ\llν$ a.s., are provided. Two CLT's are proved as well. The first deals with stable convergence while the second concerns total variation distance.

No reviews yet.

No comments yet.