Alternatives and similar repositories for mlbop:

Users that are interested in mlbop are comparing it to the libraries listed below

• probml / pmtksupport
  Various packages used by PMTK.
  ☆54Updated 5 years ago
• dillonalaird / pysvihmm
  Implementation of stochastic variational inference for Bayesian hidden Markov models.
  ☆66Updated 7 years ago
• michaelchughes / NPBayesHMM
  Nonparametric Bayesian Inference for Sequential Data. Includes state-of-the-art MCMC inference for Beta process Hidden Markov Models (BP…
  ☆78Updated 7 years ago
• SheffieldML / deepGP
  Deep Gaussian Processes in matlab
  ☆91Updated 3 years ago
• TENGBINN / Bayes-by-Backprop
  TensorFlow implementation of Bayes-by-Backprop algorithm from "Weight Uncertainty in Neural Networks" paper
  ☆51Updated 6 years ago
• anhncs / Probabilistic-Graphical-Models
  Code for Implementation, Inference, and Learning of Bayesian and Markov Networks along with some practical examples.
  ☆104Updated 12 years ago
• kamperh / bayes_gmm
  Bayesian Gaussian mixture models in Python.
  ☆64Updated last year
• rmgarnett / active_learning
  Active learning toolbox for MATLAB
  ☆46Updated 7 years ago
• rmgarnett / gpml_extensions
  Provides various extensions to the GPML toolbox for Gaussian process inference in MATLAB.
  ☆31Updated 7 years ago
• ebonilla / mtgp
  Multi-task Gaussian Process
  ☆43Updated 10 years ago
• thangbui / deepGP_approxEP
  see https://github.com/thangbui/geepee for a faster implementation
  ☆37Updated 7 years ago
• zi-w / GP-EST
  Bayesian optimization with the Gaussian process assumption
  ☆30Updated 8 years ago
• thangbui / geepee
  A collection of Gaussian process models
  ☆30Updated 7 years ago
• marionmari / pyGPs
  pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.
  ☆214Updated 6 years ago
• dustinvtran / bayesrl
  A Python library for reinforcement learning using Bayesian approaches
  ☆54Updated 9 years ago
• RuiShu / nn-bayesian-optimization
  We use a modified neural network instead of Gaussian process for Bayesian optimization.
  ☆108Updated 7 years ago
• DrugowitschLab / VBLinLogit
  Variational Bayes linear and logistic regression
  ☆34Updated 5 years ago
• jluttine / variational-bayes-book
  Open access book on variational Bayesian methods written collaboratively
  ☆28Updated 9 years ago
• rvlasveld / oc_svm
  One-Class SVM implementations for temporal segmentation of human activities
  ☆54Updated 11 years ago
• SheffieldML / GPmat
  Matlab implementations of Gaussian processes and other machine learning tools.
  ☆137Updated 7 years ago
• djour / PyBRML
  Python Version of BRML toolbox for Bayesian Reasoning and Machine Learning
  ☆163Updated 9 years ago
• siemens / policy_search_bb-alpha
  ☆68Updated 6 years ago
• locuslab / newton_admm
  A Newton ADMM based solver for Cone programming.
  ☆38Updated 7 years ago
• akshaykgupta / SG_MCMC
  Implementation of Stochastic Gradient MCMC algorithms
  ☆41Updated 8 years ago
• QuantumLiu / matDL
  A lightweight MATLAB deeplearning toolbox,based on gpuArray.
  ☆54Updated 7 years ago
• ubcmatlabguide / ubcmatlabguide.github.io
  YAGTOM: Yet Another Guide TO Matlab
  ☆56Updated 11 years ago
• t-vi / candlegp
  Gaussian Processes in Pytorch
  ☆75Updated 5 years ago
• paulorauber / pgm
  Probabilistic graphical models in python
  ☆23Updated 6 years ago
• gpschool / gpss17
  Gaussian Process and Uncertainty Quantification Summer School 2017
  ☆26Updated 2 years ago
• trungngv / gprn
  Code for the paper 'Efficient Variational Inference for Gaussian Process Regression Networks'
  ☆22Updated 11 years ago