Nonparametric Bayesian optimal designs for unit exponential nonlinear model

Nonlinear regression models have widespread applications across diverse scientific disciplines‎. ‎Achieving precise fitting of the optimal nonlinear model is essential‎, ‎taking into account the biases inherent in Bayesian optimal design‎. ‎This study introduces a Bayesian optimal design utilizing the Dirichlet process as a prior‎. ‎The Dirichlet process is a fundamental tool in exploring Nonparametric Bayesian inference‎, ‎providing multiple well-suited representations‎. ‎The research paper presents a novel one-parameter model‎, ‎termed the ``unit-exponential distribution"‎, ‎specifically designed for the unit interval‎. ‎Additionally‎, ‎a representation is employed to approximate the D-optimality criterion‎, ‎considering the Dirichlet process as a functional tool‎. ‎Through this approach‎, ‎the aim is to identify a nonparametric Bayesian optimal design.