Ecosyste.ms: Repos
An open API service providing repository metadata for many open source software ecosystems.
GitHub topics: bayesian-experimental-design
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: about 2 months ago - Pushed: almost 7 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: 8 months ago - Pushed: over 2 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: 8 months ago - Pushed: almost 4 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: 8 months ago - Pushed: about 3 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: 8 months ago - Pushed: over 3 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: 8 months ago - Pushed: about 3 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: about 1 year ago - Pushed: over 2 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: about 1 year ago - Pushed: over 2 years ago - Stars: 0 - Forks: 0
