Nonparametric Bayesian optimal designs for unit exponential regression model with respect to prior processes (with Polya Urn scheme as the base measure)

Nonlinear regression models find extensive applications across various scientific disciplines. It is crucial to accurately fit the optimal nonlinear model while taking into account the biases inherent in the Bayesian optimal design. By utilizing the Dirichlet process as a prior, we present a Bayesian optimal design. The Dirichlet process serves as a fundamental tool in the exploration of Nonparametric Bayesian inference, offering multiple representations that are well-suited for application. This research paper introduces a novel one-parameter model, referred to as the "Unit-Exponential distribution", specifically designed for the unit interval. Additionally, we employ a representation to approximate the D-optimality criterion, considering the Dirichlet process as a functional tool. Through this approach, we aim to identify a Nonparametric Bayesian optimal design.

کلیدواژه ها:

D-optimal design ، Nonparametric Bayesian optimal design ، Unit Exponential model (UE)