GPstuff

The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods. The GPstuff toolbox works (at least) with Matlab versions r2009b (7.9) or newer (older versions down to 7.7 should work also, but the code is not tested with them). Most of the functionality works also with Octave (3.6.4 or newer, see release notes for details). Most of the code is written in m-files but some of the most computationally critical parts have been coded in C.


References in zbMATH (referenced in 21 articles )

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  1. Rodriguez, Sergio; Ludkovski, Michael: Probabilistic bisection with spatial metamodels (2020)
  2. Solin, Arno; Särkkä, Simo: Hilbert space methods for reduced-rank Gaussian process regression (2020)
  3. Hartmann, Marcelo; Vanhatalo, Jarno: Laplace approximation and natural gradient for Gaussian process regression with heteroscedastic Student-(t) model (2019)
  4. Järvenpää, Marko; Gutmann, Michael U.; Pleska, Arijus; Vehtari, Aki; Marttinen, Pekka: Efficient acquisition rules for model-based approximate Bayesian computation (2019)
  5. Seongil Jo; Taeryon Choi; Beomjo Park; Peter Lenk: bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors (2019) not zbMATH
  6. Seth, Sohan; Murray, Iain; Williams, Christopher K. I.: Model criticism in latent space (2019)
  7. Gómez-Rubio, Virgilio; Rue, Håvard: Markov chain Monte Carlo with the integrated nested Laplace approximation (2018)
  8. Järvenpää, Marko; Gutmann, Michael U.; Vehtari, Aki; Marttinen, Pekka: Gaussian process modelling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria (2018)
  9. Linero, Antonio R.: Bayesian regression trees for high-dimensional prediction and variable selection (2018)
  10. Peters, Markus; Saar-Tsechansky, Maytal; Ketter, Wolfgang; Williamson, Sinead A.; Groot, Perry; Heskes, Tom: A scalable preference model for autonomous decision-making (2018)
  11. Schulz, Eric; Speekenbrink, Maarten; Krause, Andreas: A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions (2018)
  12. Aderhold, Andrej; Husmeier, Dirk; Grzegorczyk, Marco: Approximate Bayesian inference in semi-mechanistic models (2017)
  13. Matthews, Alexander G. De G.; van der Wilk, Mark; Nickson, Tom; Fujii, Keisuke; Boukouvalas, Alexis; León-Villagrá, Pablo; Ghahramani, Zoubin; Hensman, James: GPflow: a Gaussian process library using TensorFlow (2017)
  14. Li, Longhai; Qiu, Shi; Zhang, Bei; Feng, Cindy X.: Approximating cross-validatory predictive evaluation in Bayesian latent variable models with integrated IS and WAIC (2016)
  15. Terrance Savitsky: Bayesian Nonparametric Mixture Estimation for Time-Indexed Functional Data in R (2016) not zbMATH
  16. Mononen, Tommi: A case study of the widely applicable Bayesian information criterion and its optimality (2015)
  17. Neumann, Marion; Huang, Shan; Marthaler, Daniel E.; Kersting, Kristian: pyGPs -- a Python library for Gaussian process regression and classification (2015)
  18. Yang, Xufeng; Liu, Yongshou; Zhang, Yishang; Yue, Zhufeng: Hybrid reliability analysis with both random and probability-box variables (2015)
  19. Riihimäki, Jaakko; Vehtari, Aki: Laplace approximation for logistic Gaussian process density estimation and regression (2014)
  20. Vanhatalo, Jarno; Riihimäki, Jaakko; Hartikainen, Jouni; Jylänki, Pasi; Tolvanen, Ville; Vehtari, Aki: GPstuff: Bayesian modeling with Gaussian processes (2013)

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