On Metrizing Vague Convergence of Random Measures with Applications on Bayesian Nonparametric Models

This paper deals with studying vague convergence of random measures of the form $μ_{n}=\sum_{i=1}^{n} p_{i,n} δ_{θ_i}$, where $(θ_i)_{1\le i \le n}$ is a sequence of independent and identically distributed random variables with common distribution $Π$, $(p_{i,n})_{1 \le i \le n}$ are random variables chosen according to certain procedures and are independent of $(θ_i)_{i \geq 1}$ and $δ_{θ_i}$ denotes the Dirac measure at $θ_i$. We show that $μ_{n}$ converges vaguely to $μ=\sum_{i=1}^{\infty} p_{i} δ_{θ_i}$ if and only if $μ^{(k)}_{n}=\sum_{i=1}^{k} p_{i,n} δ_{θ_i}$ converges vaguely to $μ^{(k)}=\sum_{i=1}^{k} p_{i} δ_{θ_i}$ for all $k$ fixed. The limiting process $μ$ plays a central role in many areas in statistics, including Bayesian nonparametric models. A finite approximation of the beta process is derived from the application of this result. A simulated example is incorporated, in which the proposed approach exhibits an excellent performance over several existing algorithms.

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