GitHub topics: bayesian-experimental-design
yasirbarlas/RL-BOED
We investigate several reinforcement learning algorithms on three Bayesian experimental design problems. Performance is measured by each agent's training time and generalisability to various experimental setups at evaluation time.
Language: Python - Size: 438 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 3 - Forks: 1
HIIT/knowledge-elicitation-for-linear-regression
Knowledge elicitation when the user can give feedback to different features of the model with the goal to improve the prediction on the test data in a "smal n, large p" setting.
Language: Matlab - Size: 8.72 MB - Last synced at: about 1 year ago - Pushed at: almost 8 years ago - Stars: 8 - Forks: 2
gutmanngroup/idad Fork of desi-ivanova/idad
Python code for "Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods", NeurIPS, 2021, https://proceedings.neurips.cc/paper/2021/hash/d811406316b669ad3d370d78b51b1d2e-Abstract.html
Size: 72.3 KB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0
gutmanngroup/seqbed Fork of stevenkleinegesse/seqbed
Code for the paper "Sequential Bayesian Experimental Design for Implicit Models via Mutual Information", Bayesian Analysis 2021, https://arxiv.org/abs/2003.09379.
Size: 22.5 KB - Last synced at: over 1 year ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 0
gutmanngroup/GradBED Fork of stevenkleinegesse/GradBED
Code for the paper "Gradient-Based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds" https://arxiv.org/abs/2105.04379
Size: 1.47 MB - Last synced at: over 1 year ago - Pushed at: about 4 years ago - Stars: 0 - Forks: 0
gutmanngroup/bedimplicit Fork of stevenkleinegesse/bedimplicit
Source code for "Efficient Bayesian Experimental Design for Implicit Models", AISTATS 2019, https://arxiv.org/abs/1810.09912
Size: 1.21 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0
gutmanngroup/minebed Fork of stevenkleinegesse/minebed
Source code for Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation, ICML 2020, https://arxiv.org/abs/2002.08129
Size: 2.44 MB - Last synced at: over 1 year ago - Pushed at: about 4 years ago - Stars: 1 - Forks: 0
sverdoot/regularized-dpp
Implementation of Bayesian experimental design using regularized determinantal point processes
Language: Jupyter Notebook - Size: 2.06 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0
mariobecerra/i_opt_mixture_choice_models_code
Code for paper "Bayesian I-optimal designs for choice experiments with mixtures" by Mario Becerra and Peter Goos.
Language: R - Size: 41 KB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0
