Variational Bayesian optimal experimental design

Variational Bayesian optimal experimental design

Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by the difficulty of obtaining accurate estimates of the expected information gain (EIG) of an experiment. To address this, w...

Podrobná bibliografie
Hlavní autoři:	Foster, A, Jankowiak, M, Bingham, E, Horsfall, P, Tee, YW, Rainforth, T, Goodman, N
Médium:	Conference item
Vydáno:	Conference on Neural Information Processing Systems 2019

Podobné jednotky

A unified stochastic gradient approach to designing Bayesian-optimal experiments
Autor: Foster, A, a další
Vydáno: (2020)

Modern Bayesian experimental design
Autor: Rainforth, T, a další
Vydáno: (2024)

Variational, Monte Carlo and policy-based approaches to Bayesian experimental design
Autor: Foster, AE
Vydáno: (2021)

Making better use of unlabelled data in Bayesian Active learning
Autor: Bickford Smith, F, a další
Vydáno: (2024)

Bayesian Optimization for Probabilistic Programs
Autor: Rainforth, T, a další
Vydáno: (2016)

Target–aware Bayesian inference: how to beat optimal conventional estimators
Autor: Rainforth, T, a další
Vydáno: (2020)

Optimal Bayesian experimental design in the presence of model error
Autor: Feng, Chi, S.M. Massachusetts Institute of Technology
Vydáno: (2015)

SelfCheck: using LLMs to zero-shot check their own step-by-step reasoning
Autor: Miao, N, a další
Vydáno: (2024)

Numerical approaches for sequential Bayesian optimal experimental design
Autor: Huan, Xun
Vydáno: (2016)

Bayesian Optimization for Adaptive Experimental Design: A Review
Autor: Stewart Greenhill, a další
Vydáno: (2020-01-01)

Beyond Bayesian model averaging over paths in probabilistic programs with stochastic support
Autor: Reichelt, T, a další
Vydáno: (2024)

Simulation-based optimal Bayesian experimental design for nonlinear systems
Autor: Huan, Xun, a další
Vydáno: (2015)

GRADIENT-BASED STOCHASTIC OPTIMIZATION METHODS IN BAYESIAN EXPERIMENTAL DESIGN
Autor: Huan, Xun, a další
Vydáno: (2015)

Bayesian optimization with known experimental and design constraints for chemistry applications
Autor: Hase, Florian
Vydáno: (2022)

Bayesian optimization with adaptive surrogate models for automated experimental design
Autor: Bowen Lei, a další
Vydáno: (2021-12-01)

A combinatorial approach to goal-oriented optimal Bayesian experimental design
Autor: Li, Fengyi,S.M.Massachusetts Institute of Technology.
Vydáno: (2019)

Disentangling disentanglement in variational autoencoders
Autor: Mathieu, E, a další
Vydáno: (2019)

Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification
Autor: Blanchard, Antoine, a další
Vydáno: (2022)

Bayesian Optimal Experimental Design for Race Tracking in Resin Transfer Moulding
Autor: Nicholas Wright, a další
Vydáno: (2023-10-01)

Automating inference, learning, and design using probabilistic programming
Autor: Rainforth, T
Vydáno: (2017)

Fully Bayesian Experimental Design for Pharmacokinetic Studies
Autor: Elizabeth G. Ryan, a další
Vydáno: (2015-03-01)

SMC Samplers for Bayesian Optimal Nonlinear Design
Autor: Kuck, H, a další
Vydáno: (2006)

Divide, conquer, and combine: a new inference strategy for probabilistic programs with stochastic support
Autor: Zhou, Y, a další
Vydáno: (2020)

SMC samplers for Bayesian optimal nonlinear design
Autor: Kueck, H, a další
Vydáno: (2006)

An Iterative Nonlinear Filter Using Variational Bayesian Optimization
Autor: Yumei Hu, a další
Vydáno: (2018-12-01)

Online variational filtering and parameter learning
Autor: Campbell, A, a další
Vydáno: (2022)

Accounting for Computational Expenditures in Bayesian Experimental Design
Autor: Zheng, Sue
Vydáno: (2022)

Opening Design using Bayesian Optimization
Autor: Nick Vitsas, a další
Vydáno: (2023-12-01)

Towards a theoretical understanding of the robustness of variational autoencoders
Autor: Camuto, A, a další
Vydáno: (2021)

Meta-analysis of the experimental coefficient of variation in wheat using the Bayesian and Frequentist approaches
Autor: Maicon Nardino, a další
Vydáno: (2022-06-01)

A Bayesian interactive optimization approach to procedural animation design
Autor: Brochu, E, a další
Vydáno: (2010)

Optimizing training trajectories in variational autoencoders via latent Bayesian optimization approach
Autor: Arpan Biswas, a další
Vydáno: (2023-01-01)

Automation of Experimental Modal Analysis Using Bayesian Optimization
Autor: Johannes Ellinger, a další
Vydáno: (2023-01-01)

Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions.
Autor: Jaron C Thompson, a další
Vydáno: (2023-09-01)

Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions
Autor: Jaron C. Thompson, a další
Vydáno: (2023-09-01)

Optimal experiment design with adjoint-accelerated Bayesian inference
Autor: Matthew Yoko, a další
Vydáno: (2024-01-01)

Accelerated Bayesian experimental design for chemical kinetic models
Autor: Huan, Xun
Vydáno: (2010)

Bayesian Inference and Experimental Design of Combustion Kinetic Models
Autor: Chen, Huaibo
Vydáno: (2024)

Automated data acquisition via Bayesian experimental design
Autor: Ivanova, DR
Vydáno: (2024)

Bayesian experimental design and parameter estimation for ultrafast spin dynamics
Autor: Zhantao Chen, a další
Vydáno: (2023-01-01)