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Updated: Feb 5, 2026

Computerized Adaptive Testing System of Functional Assessment of Stroke
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Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions.

Hongqiao Wang1, Jinglai Li2

  • 1Institute of Natural Sciences and School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China wanghongqiao@sjtu.edu.cn.

Neural Computation
|September 15, 2018
PubMed
Summary
This summary is machine-generated.

We introduce a Gaussian process (GP) method to approximate complex Bayesian inference problems. This approach improves computational efficiency for models with intensive likelihood functions.

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Area of Science:

  • Computational Statistics
  • Machine Learning
  • Bayesian Inference

Background:

  • Bayesian inference problems often involve computationally intensive likelihood functions, posing a significant challenge for analysis.
  • Existing methods for Bayesian computation may struggle with the complexity and computational demands of these likelihoods.

Purpose of the Study:

  • To develop a novel Gaussian process (GP)-based method for approximating joint distributions in Bayesian inference.
  • To address the computational challenges posed by computationally intensive likelihood functions.

Main Methods:

  • The proposed method approximates the joint distribution of parameters and data using a Gaussian process (GP).
  • The joint density is approximated as a product of an approximate posterior density and an exponentiated GP surrogate.
  • An adaptive algorithm employing active learning selects design points for constructing the approximation.

Main Results:

  • Numerical examples demonstrate the competitive performance of the proposed GP-based method.
  • The method effectively approximates the joint distribution for Bayesian inference problems with intensive likelihoods.

Conclusions:

  • The Gaussian process (GP)-based approximation offers a computationally efficient alternative for Bayesian inference.
  • The adaptive algorithm enhances the construction of these approximations, showing promise for complex statistical modeling.