Bayesian – Page 3 – Andrew Fairless, Ph.D.

What I Read: ‘Machine Scientists’ Distill the Laws of Physics From Raw Data

By Andrew Fairless on June 7, 2022May 23, 2022

https://www.quantamagazine.org/machine-scientists-distill-the-laws-of-physics-from-raw-data-20220510/ Powerful ‘Machine Scientists’ Distill the Laws of Physics From Raw DataCharlie WoodStaff WriterMay 10, 2022 “Researchers say we’re on the cusp of “GoPro physics,” where a camera can pointContinue readingWhat I Read: ‘Machine Scientists’ Distill the Laws of Physics From Raw Data

What I Read: Generative Modeling by Estimating Gradients

By Andrew Fairless on March 7, 2022January 20, 2022

https://yang-song.github.io/blog/2021/score/ Generative Modeling by Estimating Gradients of the Data DistributionAuthor Yang SongDate May 5, 2021 “This blog post gives a detailed introduction to score-based generative models. We demonstrate that thisContinue readingWhat I Read: Generative Modeling by Estimating Gradients

What I Read: MCMC for big datasets

By Andrew Fairless on February 22, 2022January 20, 2022

https://www.pymc-labs.io/blog-posts/pymc-stan-benchmark/ MCMC for big datasets: faster sampling with JAX and the GPUScaling PyMC using JAX to sample on the GPUAuthored by Martin Ingram on 2021-12-22 “You’ll often hear people sayContinue readingWhat I Read: MCMC for big datasets

What I Read: Bayesian Geometry

By Andrew Fairless on February 21, 2022January 20, 2022

https://towardsdatascience.com/on-bayesian-geometry-7755abf9f4d2 On Bayesian GeometryGeometric interpretation of probability distributionsMichael Larionov, PhD “Bayesian inference is based on the fact that we often don’t know the underlying distribution of data, so we needContinue readingWhat I Read: Bayesian Geometry

What I Read: Gaussian Process, Active Learning in Physics

By Andrew Fairless on January 17, 2022December 11, 2021

https://towardsdatascience.com/gaussian-process-first-step-towards-active-learning-in-physics-239a8b260579?gi=41cc7e15ddd0 Gaussian Process: First Step Towards Active Learning in PhysicsMaxim ZiatdinovNov 1 “An approach to address these problems is a Gaussian Process…. we would like to offer a slightly unusualContinue readingWhat I Read: Gaussian Process, Active Learning in Physics

What I Read: what is a Gaussian process?

By Andrew Fairless on December 7, 2021November 16, 2021

https://dansblog.netlify.app/posts/2021-11-03-yes-but-what-is-a-gaussian-process-or-once-twice-three-times-a-definition-or-a-descent-into-madness/ Yes but what is a Gaussian process? or, Once, twice, three times a definition; or A descent into madnessDan SimpsonNov. 3, 2021Yes but what is a Gaussian process? or,Continue readingWhat I Read: what is a Gaussian process?

What I Read: How to assign partial credit on an exam of true-false questions

By Andrew Fairless on November 30, 2021November 16, 2021

https://terrytao.wordpress.com/2016/06/01/how-to-assign-partial-credit-on-an-exam-of-true-false-questions/ How to assign partial credit on an exam of true-false questions?By Terence Tao1 June, 2016Updates on my research and expository papers, discussion of open problems, and other maths-related topics.Continue readingWhat I Read: How to assign partial credit on an exam of true-false questions

What I Read: Machine learning is just statistics + quantifier reversal

By Andrew Fairless on November 18, 2021November 16, 2021

https://jeremybernste.in/writing/ml-is-just-statistics Machine learning is just statistics + quantifier reversaljeremybernsteLos Angeles, 21 Oct 2021 “But in machine learning there’s a switcheroo—we select the sample of data first, and then we useContinue readingWhat I Read: Machine learning is just statistics + quantifier reversal

What I Read: Bayesian Media Mix Modeling

By Andrew Fairless on October 7, 2021September 21, 2021

Bayesian Media Mix Modeling for Marketing OptimizationBenjamin Vincent2021-09-17 “Fortunately, with Bayesian modeling, we can do better than this! So-called Media Mix Modelling (MMM) can estimate how effective each advertising channelContinue readingWhat I Read: Bayesian Media Mix Modeling

What I Read: Pathfinder, A parallel quasi-Newton algorithm

By Andrew Fairless on September 2, 2021August 17, 2021

https://statmodeling.stat.columbia.edu/2021/08/10/pathfinder-a-parallel-quasi-newton-algorithm-for-reaching-regions-of-high-probability-mass/ Pathfinder: A parallel quasi-Newton algorithm for reaching regions of high probability massAndrew Gelman10 August 2021, 9:07 am “In the world of Stan, we see three roles for Pathfinder: (1)Continue readingWhat I Read: Pathfinder, A parallel quasi-Newton algorithm

Tag: Bayesian