Pyroで実践するベイズ機械学習
本書について #Pyroで実践するベイズ機械学習は、Uber AI Labsが中心となって開発を進めている確率的プログラミング言語Pyroを用いてベイズ機械学習を行う方法を解説した入門書です。ベイズ機械学習の基礎からPyroでそれをどのように実装するのかまでを解説していきます。 本ドキュメントは2021/08/08 現在、制作中です。 本ドキュメントはオープンなプロジェクトであり、そのため協力者を広く求めています。本書のソースコードは GitHub上で公開されています。 本書への追記や修正などありましたら、上記GitHubにてIssueの発行、またはPull requestをお願いいたします。 本ドキュメントは Creative Commons Attribution-ShareAlike 4.0 International License のもとで公開されています。
HackPPL: a universal probabilistic programming language | the morning paper
the morning paper a random walk through Computer Science research, by Adrian Colyer Made delightfully fast by strattic HackPPL: a universal probabilistic programming language Ai et al., MAPL’19 The Hack programming language, as the authors proudly tell us, is “a dominant web development language across large technology firms with over 100 million lines of production code.” Nail that niche! Does yo
An Introduction to Probabilistic Programming
This book is a graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. It is aimed at people who have an undergraduate-level understanding of either or, ideally, both probabilistic machine learning and programming la
The Algorithms Behind Probabilistic Programming
We recently introduced our report on probabilistic programming. The accompanying prototype allows you to explore the past and future of the New York residential real estate market. This post gives a feel for the content in our report by introducing the algorithms and technology that make probabilistic programming possible. We’ll dive even deeper into these algorithms in conversation with the Stan
確率とモナドと確率的プログラミング - Qiita
この記事はADVANCED BEGINNERからCOMPETENTの方を対象読者として書かれています。 コインの裏表やサイコロの出目はよく確率変数によって表されます。確率変数が互いに依存しているようなモデルを記述する手法としてグラフィカルモデルと云うものがあります。例えば、ある分布に従って表が出る確率が偏ったコインが選ばれた後、そのコインを投げて表裏が決まるような実験を考えた場合、コインの確率変数を $X$, コインの表裏の確率変数を $Y$ とすると、この系を記述するグラフィカルモデルは このようになります。ところで $X$ は表が出る確率Double上の確率変数RVar Doubleで、 $Y$ は $X$ の結果に依存したコインの裏表Bool上の確率変数Double -> RVar Boolであると考えるとします。今コインがランダムに選ばれると言う構造を 忘れて コインの表裏が出る確
Probabilistic Programming
The programming languages and machine learning communities have, over the last few years, developed a shared set of research interests under the umbrella of probabilistic programming. The idea is that we might be able to “export” powerful PL concepts like abstraction and reuse to statistical modeling, which is currently an arcane and arduous task. (You may want to read the most recent version of t
The Design and Implementation of Probabilistic Programming Languages
The Design and Implementation of Probabilistic Programming Languages About: Probabilistic programming languages (PPLs) unify techniques for the formal description of computation and for the representation and use of uncertain knowledge. PPLs have seen recent interest from the artificial intelligence, programming languages, cognitive science, and natural languages communities. This book explains ho
The State of Probabilistic Programming
For two weeks last July, I cocooned myself in a hotel in Portland, OR, living and breathing probabilistic programming as a “student” in the probabilistic programming summer school run by DARPA. The school is part of the broader DARPA program on Probabilistic Programming for Advanced Machine Learning (PPAML), which has resulted in a great infusion of energy (and funding) into the probabilistic prog
R2: A Probabilistic Programming System - Microsoft Research
What is R2? R2 is a probabilistic programming system that uses powerful techniques from program analysis and verification for efficient Markov Chain Monte Carlo (MCMC) inference. The language that is used to describe probabilistic models in R2 is based on C#.R2 compiles the given model into executable code to generate samples from the posterior distribution. The inference algorithm currently imple
What is probabilistic programming? - The PL Enthusiast
In this post, I introduce the emerging area of probabilistic programming, showing how probabilistic programs will hopefully make it easier to perform Bayesian-style machine learning, among other applications. Probabilistic programming is an exciting, and growing, area of research, with fantastic people in both AI/ML and PL working together and making big strides. PL methods — including formal sema
Probabilistic Models of Cognition - 2nd Edition
Probabilistic Models of Cognition by Noah D. Goodman, Joshua B. Tenenbaum & The ProbMods Contributors This book explores the probabilistic approach to cognitive science, which models learning and reasoning as inference in complex probabilistic models. We examine how a broad range of empirical phenomena, including intuitive physics, concept learning, causal reasoning, social cognition, and language
Why Probabilistic Programming Matters
Probabilistic programming is a newer way of posing machine learning problems. As the models we want to create become more complex it will be necessary to embrace more generic tools for capturing dependencies. I wish to argue that probabilistic programming languages should be the dominant way we perform this modeling, and will demonstrate it by showing the variety of problems that can be trivially
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
Monad-Bayes -
Seminar Details
Tutorial: Probabilistic programming - a categorical tutorial (Sam Staton)
Soss: Probabilistic Programming with Julia
https://www.tweag.io/posts/2019-11-08-monad-bayes-2.html/
https://www.tweag.io/posts/2019-09-20-monad-bayes-1.html/
https://dl.acm.org/citation.cfm?id=2804317
https://homes.luddy.indiana.edu/ccshan/rational/system.pdf