Sigma-Point Filters based on Bayesian Quadrature
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Updated
Mar 29, 2018 - Python
Sigma-Point Filters based on Bayesian Quadrature
Normal Gaussian Process and Gaussian Process with Poisson Likelihood
Adaptive experimental design for maximizing information gain
Highly performant and scalable out-of-the-box gaussian process regression and Bernoulli classification. Built upon GPyTorch, with a familiar sklearn api.
The Docker container for MGPfact is primarily used for unsupervised manifold learning of single-cell RNA-seq data and can factorize complex cell trajectories into interpretable branching Gaussian processes.
Programming assignments and final project of stochastic processes course
Gaussian Process localization with ToF and RSSI
R Package for modeling omega-reliability coefficient from exogenous or latent space using Gaussian Processes or linear models.
Treed Gaussian process algorithm in Python
Resources and extra documentation for the manuscript "A Global Sensitivity-based Identification of Key Factors on Stability of Power Grid with Multi-outfeed HVDC" published in IEEE Latin America Transactions.
Incremental Sparse Spectrum Gaussian Process Regression
A NumPy implementation of Lee et al., Deep Neural Networks as Gaussian Processes, 2018
Data and code associated with paper "On the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditions" currently in review.
My implementation of several projects for the course "Probabilistic AI" at ETHZ in 2023, including Bayesian Optimization, Gaussian Processes and Reinforcement Learning.
Flexible Bayesian Optimization in R
Gaussian-Process Surrogate Optimisation
Quasar Factor Analysis – An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis
Bayesian Learning for Control in Multimodal Dynamical Systems | written in Org-mode
Mini Bayesian Optimization package for ACML2020 Tutorial on Bayesian Optimization
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