Gaussian Process model for fitting deterministic simulator output. Establish efficient and reliable likelihood optimization through hybridized DIRECT-BFGS and multi-start BFGS algorithms. Programming Language: Matlab.

Features

  • Returns prediction, Y, and prediction uncertainty estimate, MSE, for any number of points
  • User is free to select training data. Suggest user scales simulator input to [0,1]^d.
  • Efficient and reliable likelihood optimization.
  • 4 Likelihood optimization routines: DIRECT-BFGS, DIRECT-IF, 0.5d multi-start BFGS, and 2d+1 multi-start BFGS
  • Squared exponential correlation matrix, R.
  • Addition of 'nugget' to R for improved stability of R^-1 and |R| computation.
  • Lower bound on nugget to minimize over-smoothing.
  • Iterative regularization method for improved accuracy when using a nugget.

Project Samples

Project Activity

See All Activity >

Follow GPMfit

GPMfit Web Site

Other Useful Business Software
Gen AI apps are built with MongoDB Atlas Icon
Gen AI apps are built with MongoDB Atlas

Build gen AI apps with an all-in-one modern database: MongoDB Atlas

MongoDB Atlas provides built-in vector search and a flexible document model so developers can build, scale, and run gen AI apps without stitching together multiple databases. From LLM integration to semantic search, Atlas simplifies your AI architecture—and it’s free to get started.
Start Free
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of GPMfit!

Additional Project Details

Languages

English

Intended Audience

Engineering, Financial and Insurance Industry, Science/Research

Programming Language

MATLAB

Related Categories

MATLAB Simulation Software, MATLAB Mathematics Software, MATLAB Statistics Software

Registered

2013-08-02