NicolasDurrande / Gaussian-Processes-short-course

Related projects: ⓘ

• deepcharles / tutorials-mva-2022
  Tutorials for the Machine Learning for Time Series class - Master MVA (2021/2022)
  ☆10Updated 2 years ago
• lovvge / Forecasting-Tutorials
  ☆22Updated 3 years ago
• compops / gpo-smc-abc
  Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
  ☆12Updated 6 years ago
• gpschool / gpss18
  Gaussian Process and Uncertainty Quantification Summer School 2018
  ☆31Updated last year
• chaaland / bloomberg-ml-course
  Solutions to machine learning HW from bloomberg ml course
  ☆12Updated 5 years ago
• VBayesLab / VBLab
  Official documentation
  ☆23Updated 3 years ago
• duke-mlss / Duke-MLWS-2019
  Duke Machine Learning Winter School 2019
  ☆27Updated 5 years ago
• bifeng / Awesome-time-series
  ☆31Updated this week
• PhIMaL / DeePyMoD_tensorflow
  This implementation of DeePyMoD is no longer maintained! We switched to a PyTorch based implementation: https://github.com/PhIMaL/DeePyM…
  ☆24Updated 4 years ago
• alan-turing-institute / rbocpdms
  Robust bayesian online changepoint detection with model selection
  ☆23Updated 5 years ago
• tensorchiefs / dl_course_2020
  The 2020 Version of the Deep Learning Course
  ☆8Updated 4 years ago
• alan-turing-institute / rPSMF
  Code for Probabilistic Sequential Matrix Factorization
  ☆15Updated 3 years ago
• VietTran86 / Copula-Variational-Bayes
  Matlab code for my paper "Copula Variational Bayes inference via information geometry", submitted to IEEE Trans. on information theory, 2…
  ☆50Updated 5 years ago
• christinaheinze / causality-course-ethz
  Code used in the causality course (401-4632-15) at ETH Zurich.
  ☆19Updated 5 years ago
• NicolasDurrande / Gaussian-Process-Regression-handout
  ☆38Updated 7 years ago
• thiery-lab / manifold-mcmc-for-diffusions
  Code accompanying the paper 'Manifold MCMC methods for Bayesian inference in a wide class of diffusion models'
  ☆10Updated last year
• fonnesbeck / gp_tutorial_pydata
  PyData San Luis 2017 Tutorial: An Introduction to Gaussian Processes in PyMC3
  ☆15Updated 6 years ago
• ngorbach / Variational_Gradient_Matching_for_Dynamical_Systems
  Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"
  ☆26Updated 5 years ago
• alan-turing-institute / bocpdms
  ☆31Updated 5 years ago
• nickfrosst / neural_additive_models
  stand alone Neural Additive Models, forked from google-reasearch for easy import to colab
  ☆26Updated 3 years ago
• nla-group / ABBA-LSTM
  ☆15Updated last year
• hstrey / Ornstein-Uhlenbeck-Bayesian
  ☆18Updated 4 years ago
• bsouhaib / prob-hts
  Source code for Hierarchical Probabilistic Forecasting of Electricity Demand with Smart Meter Data by Ben Taieb, Souhaib, Taylor, James, …
  ☆10Updated 5 years ago
• iankurgarg / Reinforcement-Learning-Feature-Selection
  Feature selection for maximizing expected cumulative reward
  ☆29Updated 6 years ago
• big-data-lab-umbc / ensemble_causality_learning
  ☆10Updated 4 years ago
• Ralami1859 / BayesianOnlineChange-pointDetection-python-codes-
  Implementation of the Bayesian Online Change-point Detector of Ryan Prescott Adams and David McKay.
  ☆14Updated 3 years ago
• chlubba / op_importance
  Compute set of important operations for HCTSA code
  ☆24Updated 4 years ago
• zgbkdlm / ssdgp
  State-space deep Gaussian processes in Python and Matlab
  ☆29Updated 2 years ago
• mollenhauerm / keras-temporal-autoencoder
  Keras framework for autocovariance-based dimensionality reduction of time series data with deep neural networks.
  ☆30Updated 6 years ago
• vcerqueira / MLforForecasting
  Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters
  ☆38Updated 4 years ago