The Net Advance of Physics: BAYESIAN METHODS

The Net Advance of Physics:
BAYESIAN METHODS

BAYESIAN METHODS:

General: D'Agostini 2003/04;
- Topics in statistical data analysis for high-energy physics by G. Cowan [2009/06]
- How to Use Experimental Data to Compute the Probability of Your Theory by Georgios Choudalakis [2011/10]
- Measurement Error Models in Astronomy by Brandon C. Kelly [2011/06]
- Understanding better (some) astronomical data using Bayesian methods by Stefano Andreon [2011/12]
- Frequentism and Bayesianism: A Python-driven Primer by Jake VanderPlas [2014/11]
- Bayesian Statistics in Software Engineering: Practical Guide and Case Studies by Carlo A. Furia [2016/08]
- Three Lectures on Probability and Statistics by Carlos Mana [2016/10]
- To Bayes or not to Bayes? That's no longer the question! by Ernest Fokoue [2018/05]
- A Guide to General-Purpose Approximate Bayesian Computation Software by Athanasios Kousathanas et al. [2018/06]
- Lectures on Statistics in Theory: Prelude to Statistics in Practice by Robert D. Cousins [2018/07]
- An introduction to Bayesian inference in gravitational-wave astronomy: parameter estimation, model selection, and hierarchical models by Eric Thrane and Colm Talbot [2018/09]
- Bayesian Regularization: From Tikhonov to Horseshoe by Nicholas G. Polson and Vadim Sokolov [2019/02]
- Introducing Bayesian Analysis with M&Ms: An Active-Learning Exercise for Undergraduates by Gwendolyn Eadie et al. [2019/04]
- A Review of Approximate Bayesian Computation Methods via Density Estimation: Inference for Simulator-Models by Clara Grazian and Yanan Fan [2019/09]
- Bayesian Design of Experiments: Implementation, Validation and Application to Chemical Kinetics by Eric A. Walker and Kishore Ravisankar [2019/09]
Type: HIERARCHICAL:
- One, no one, and one hundred thousand: Inferring the properties of a population in presence of selection effects by S. Vitale [2020/07]
Type: QUANTUM:
- An Introduction to Quantum Bayesian Networks for Mixed States by Robert R. Tucci [2012/04]
Re: ALGEBRAIC GEOMETRY:
- Recent advances in algebraic geometry and Bayesian statistics by S. Watanabe [2022/11]
Re: ASTROPHYSICS:
- Bayesian Model Averaging in Astrophysics: A Review by David Parkinson and Andrew R. Liddle [Statistical Analysis and Data Mining 6, 3 (2013)]
- Bayesian Coronal Seismology by Iñigo Arregui [2017/09]
- Bayesian Methods for Exoplanet Science by Hannu Parviainen [2017/11]
- An introduction to Bayesian inference in gravitational-wave astronomy: parameter estimation, model selection, and hierarchical models by Eric Thrane and Colm Talbot [2018/09]
Re: BIOSCIENCES:
- Approximation and inference methods for stochastic biochemical kinetics - a tutorial review by David Schnoerr et al. [Journal of Physics A 50, 093001 (2017)]
Re: COMPUTER SCIENCE:
- Naive Bayes and Text Classification I - Introduction and Theory by Sebastian Raschka [2014/10]
- Bayesian Statistics in Software Engineering: Practical Guide and Case Studies by Carlo A. Furia [2016/08]
- A tutorial on Bayesian data assimilation by C. Grudzien and M. Bocquet [2021/12]
Re: COSMOLOGY:
- Bayes in the sky: Bayesian inference and model selection in cosmology by Roberto Trotta [2008/03]
- Bayesian Methods in Cosmology by Roberto Trotta [2014/03]
Re: FISHER INFORMATION:
- A Tutorial on Fisher Information by Alexander Ly et al. [2017/05]
Re: HIGH-ENERGY PHYSICS:
- Applications of emulation and Bayesian methods in heavy-ion physics by J.-F. Paquet [2023/10]
Re: MARKOV CHAIN MONTE CARLO:
- A Conceptual Introduction to Markov Chain Monte Carlo Methods by Joshua S. Speagle [2019/09]
Re: MATERIAL SCIENCE:
- Bayesian optimization in materials science: A survey by L. Kotthoff et al. [2021/08]
Re: NEURAL NETWORKS:
- Bayesian neural networks by T. Charnock et al. [2020/06]
- A primer on Bayesian neural networks: Review and debates by J. Arbel et al. [2023/09]
Re: OPTIMISATION:
- A Tutorial on Bayesian Optimization by Peter I. Frazier [2018/07]
- Bayesian optimization in materials science: A survey by L. Kotthoff et al. [2021/08]

THE NET ADVANCE OF PHYSICS