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Stan: a Probabilistic Programming Language
JSS Journal of Statistical Software January 2017, Volume 76, Issue 1. doi: 10.18637/jss.v076.i01 Stan: A Probabilistic Programming Language Bob Carpenter Andrew Gelman Matthew D. Hoffman Columbia University Columbia University Adobe Creative Technologies Lab Daniel Lee Ben Goodrich Michael Betancourt Columbia University Columbia University Columbia University Marcus A. Brubaker Jiqiang Guo Peter Li York University NPD Group Columbia University Allen Riddell Indiana University Abstract Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propa- gation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces sup- port sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting. -
Stan: a Probabilistic Programming Language
JSS Journal of Statistical Software MMMMMM YYYY, Volume VV, Issue II. http://www.jstatsoft.org/ Stan: A Probabilistic Programming Language Bob Carpenter Andrew Gelman Matt Hoffman Columbia University Columbia University Adobe Research Daniel Lee Ben Goodrich Michael Betancourt Columbia University Columbia University University of Warwick Marcus A. Brubaker Jiqiang Guo Peter Li University of Toronto, NPD Group Columbia University Scarborough Allen Riddell Dartmouth College Abstract Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.2.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propa- gation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line, through R using the RStan package, or through Python using the PyStan package. All three interfaces support sampling and optimization-based inference. RStan and PyStan also provide access to log probabilities, gradients, Hessians, and data I/O. Keywords: probabilistic program, Bayesian inference, algorithmic differentiation, Stan. -
Advancedhmc.Jl: a Robust, Modular and Efficient Implementation of Advanced HMC Algorithms
2nd Symposium on Advances in Approximate Bayesian Inference, 20191{10 AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms Kai Xu [email protected] University of Edinburgh Hong Ge [email protected] University of Cambridge Will Tebbutt [email protected] University of Cambridge Mohamed Tarek [email protected] UNSW Canberra Martin Trapp [email protected] Graz University of Technology Zoubin Ghahramani [email protected] University of Cambridge & Uber AI Labs Abstract Stan's Hamilton Monte Carlo (HMC) has demonstrated remarkable sampling robustness and efficiency in a wide range of Bayesian inference problems through carefully crafted adaption schemes to the celebrated No-U-Turn sampler (NUTS) algorithm. It is challeng- ing to implement these adaption schemes robustly in practice, hindering wider adoption amongst practitioners who are not directly working with the Stan modelling language. AdvancedHMC.jl (AHMC) contributes a modular, well-tested, standalone implementation of NUTS that recovers and extends Stan's NUTS algorithm. AHMC is written in Julia, a modern high-level language for scientific computing, benefiting from optional hardware acceleration and interoperability with a wealth of existing software written in both Julia and other languages, such as Python. Efficacy is demonstrated empirically by comparison with Stan through a third-party Markov chain Monte Carlo benchmarking suite. 1. Introduction Hamiltonian Monte Carlo (HMC) is an efficient Markov chain Monte Carlo (MCMC) algo- rithm which avoids random walks by simulating Hamiltonian dynamics to make proposals (Duane et al., 1987; Neal et al., 2011). Due to the statistical efficiency of HMC, it has been widely applied to fields including physics (Duane et al., 1987), differential equations (Kramer et al., 2014), social science (Jackman, 2009) and Bayesian inference (e.g. -
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a platform for all that we know savas parastatidis http://savas.me savasp transition from web to apps increasing focus on information (& knowledge) rise of personal digital assistants importance of near-real time processing http://aptito.com/blog/wp-content/uploads/2012/05/smartphone-apps.jpg today... storing computing computers are huge amounts great tools for of data managing indexing example google and microsoft both have copies of the entire web (and more) for indexing purposes tomorrow... storing computing computers are huge amounts great tools for of data managing indexing acquisition discovery aggregation organization we would like computers to of the world’s information also help with the automatic correlation analysis and knowledge interpretation inference data information knowledge intelligence wisdom expert systems watson freebase wolframalpha rdbms google now web indexing data is symbols (bits, numbers, characters) information adds meaning to data through the introduction of relationship - it answers questions such as “who”, “what”, “where”, and “when” knowledge is a description of how the world works - it’s the application of data and information in order to answer “how” questions G. Bellinger, D. Castro, and A. Mills, “Data, Information, Knowledge, and Wisdom,” Inform. pp. 1–4, 2004 web – the data platform web – the information platform web – the knowledge platform foundation for new experiences “wisdom is not a product of schooling but of the lifelong attempt to acquire it” representative examples wolframalpha watson source: -
Ontologies and Languages for Representing Mathematical Knowledge on the Semantic Web
Ontologies and Languages for Representing Mathematical Knowledge on the Semantic Web Editor(s): Aldo Gangemi, ISTC-CNR Rome, Italy Solicited review(s): Claudio Sacerdoti Coen, University of Bologna, Italy; Alexandre Passant, DERI, National University of Galway, Ireland; Aldo Gangemi, ISTC-CNR Rome, Italy Christoph Lange data vocabularies and domain knowledge from pure and ap- plied mathematics. FB 3 (Mathematics and Computer Science), Many fields of mathematics have not yet been imple- University of Bremen, Germany mented as proper Semantic Web ontologies; however, we Computer Science, Jacobs University Bremen, show that MathML and OpenMath, the standard XML-based exchange languages for mathematical knowledge, can be Germany fully integrated with RDF representations in order to con- E-mail: [email protected] tribute existing mathematical knowledge to the Web of Data. We conclude with a roadmap for getting the mathematical Web of Data started: what datasets to publish, how to inter- link them, and how to take advantage of these new connec- tions. Abstract. Mathematics is a ubiquitous foundation of sci- Keywords: mathematics, mathematical knowledge manage- ence, technology, and engineering. Specific areas of mathe- ment, ontologies, knowledge representation, formalization, matics, such as numeric and symbolic computation or logics, linked data, XML enjoy considerable software support. Working mathemati- cians have recently started to adopt Web 2.0 environments, such as blogs and wikis, but these systems lack machine sup- 1. Introduction: Mathematics on the Web – State port for knowledge organization and reuse, and they are dis- of the Art and Challenges connected from tools such as computer algebra systems or interactive proof assistants. -
Datatone: Managing Ambiguity in Natural Language Interfaces for Data Visualization Tong Gao1, Mira Dontcheva2, Eytan Adar1, Zhicheng Liu2, Karrie Karahalios3
DataTone: Managing Ambiguity in Natural Language Interfaces for Data Visualization Tong Gao1, Mira Dontcheva2, Eytan Adar1, Zhicheng Liu2, Karrie Karahalios3 1University of Michigan, 2Adobe Research 3University of Illinois, Ann Arbor, MI San Francisco, CA Urbana Champaign, IL fgaotong,[email protected] fmirad,[email protected] [email protected] ABSTRACT to be both flexible and easy to use. General purpose spread- Answering questions with data is a difficult and time- sheet tools, such as Microsoft Excel, focus largely on offer- consuming process. Visual dashboards and templates make ing rich data transformation operations. Visualizations are it easy to get started, but asking more sophisticated questions merely output to the calculations in the spreadsheet. Asking often requires learning a tool designed for expert analysts. a “visual question” requires users to translate their questions Natural language interaction allows users to ask questions di- into operations on the spreadsheet rather than operations on rectly in complex programs without having to learn how to the visualization. In contrast, visual analysis tools, such as use an interface. However, natural language is often ambigu- Tableau,1 creates visualizations automatically based on vari- ous. In this work we propose a mixed-initiative approach to ables of interest, allowing users to ask questions interactively managing ambiguity in natural language interfaces for data through the visualizations. However, because these tools are visualization. We model ambiguity throughout the process of often intended for domain specialists, they have complex in- turning a natural language query into a visualization and use terfaces and a steep learning curve. algorithmic disambiguation coupled with interactive ambigu- Natural language interaction offers a compelling complement ity widgets. -
Maple Advanced Programming Guide
Maple 9 Advanced Programming Guide M. B. Monagan K. O. Geddes K. M. Heal G. Labahn S. M. Vorkoetter J. McCarron P. DeMarco c Maplesoft, a division of Waterloo Maple Inc. 2003. ii ¯ Maple, Maplesoft, Maplet, and OpenMaple are trademarks of Water- loo Maple Inc. c Maplesoft, a division of Waterloo Maple Inc. 2003. All rights re- served. The electronic version (PDF) of this book may be downloaded and printed for personal use or stored as a copy on a personal machine. The electronic version (PDF) of this book may not be distributed. Information in this document is subject to change without notice and does not rep- resent a commitment on the part of the vendor. The software described in this document is furnished under a license agreement and may be used or copied only in accordance with the agreement. It is against the law to copy the software on any medium as specifically allowed in the agreement. Windows is a registered trademark of Microsoft Corporation. Java and all Java based marks are trademarks or registered trade- marks of Sun Microsystems, Inc. in the United States and other countries. Maplesoft is independent of Sun Microsystems, Inc. All other trademarks are the property of their respective owners. This document was produced using a special version of Maple that reads and updates LATEX files. Printed in Canada ISBN 1-894511-44-1 Contents Preface 1 Audience . 1 Worksheet Graphical Interface . 2 Manual Set . 2 Conventions . 3 Customer Feedback . 3 1 Procedures, Variables, and Extending Maple 5 Prerequisite Knowledge . 5 In This Chapter . -
End-User Probabilistic Programming
End-User Probabilistic Programming Judith Borghouts, Andrew D. Gordon, Advait Sarkar, and Neil Toronto Microsoft Research Abstract. Probabilistic programming aims to help users make deci- sions under uncertainty. The user writes code representing a probabilistic model, and receives outcomes as distributions or summary statistics. We consider probabilistic programming for end-users, in particular spread- sheet users, estimated to number in tens to hundreds of millions. We examine the sources of uncertainty actually encountered by spreadsheet users, and their coping mechanisms, via an interview study. We examine spreadsheet-based interfaces and technology to help reason under uncer- tainty, via probabilistic and other means. We show how uncertain values can propagate uncertainty through spreadsheets, and how sheet-defined functions can be applied to handle uncertainty. Hence, we draw conclu- sions about the promise and limitations of probabilistic programming for end-users. 1 Introduction In this paper, we discuss the potential of bringing together two rather distinct approaches to decision making under uncertainty: spreadsheets and probabilistic programming. We start by introducing these two approaches. 1.1 Background: Spreadsheets and End-User Programming The spreadsheet is the first \killer app" of the personal computer era, starting in 1979 with Dan Bricklin and Bob Frankston's VisiCalc for the Apple II [15]. The primary interface of a spreadsheet|then and now, four decades later|is the grid, a two-dimensional array of cells. Each cell may hold a literal data value, or a formula that computes a data value (and may depend on the data in other cells, which may themselves be computed by formulas). -
An Introduction to Data Analysis Using the Pymc3 Probabilistic Programming Framework: a Case Study with Gaussian Mixture Modeling
An introduction to data analysis using the PyMC3 probabilistic programming framework: A case study with Gaussian Mixture Modeling Shi Xian Liewa, Mohsen Afrasiabia, and Joseph L. Austerweila aDepartment of Psychology, University of Wisconsin-Madison, Madison, WI, USA August 28, 2019 Author Note Correspondence concerning this article should be addressed to: Shi Xian Liew, 1202 West Johnson Street, Madison, WI 53706. E-mail: [email protected]. This work was funded by a Vilas Life Cycle Professorship and the VCRGE at University of Wisconsin-Madison with funding from the WARF 1 RUNNING HEAD: Introduction to PyMC3 with Gaussian Mixture Models 2 Abstract Recent developments in modern probabilistic programming have offered users many practical tools of Bayesian data analysis. However, the adoption of such techniques by the general psychology community is still fairly limited. This tutorial aims to provide non-technicians with an accessible guide to PyMC3, a robust probabilistic programming language that allows for straightforward Bayesian data analysis. We focus on a series of increasingly complex Gaussian mixture models – building up from fitting basic univariate models to more complex multivariate models fit to real-world data. We also explore how PyMC3 can be configured to obtain significant increases in computational speed by taking advantage of a machine’s GPU, in addition to the conditions under which such acceleration can be expected. All example analyses are detailed with step-by-step instructions and corresponding Python code. Keywords: probabilistic programming; Bayesian data analysis; Markov chain Monte Carlo; computational modeling; Gaussian mixture modeling RUNNING HEAD: Introduction to PyMC3 with Gaussian Mixture Models 3 1 Introduction Over the last decade, there has been a shift in the norms of what counts as rigorous research in psychological science. -
A New Kind of Science
Wolfram|Alpha, A New Kind of Science A New Kind of Science Wolfram|Alpha, A New Kind of Science by Bruce Walters April 18, 2011 Research Paper for Spring 2012 INFSY 556 Data Warehousing Professor Rhoda Joseph, Ph.D. Penn State University at Harrisburg Wolfram|Alpha, A New Kind of Science Page 2 of 8 Abstract The core mission of Wolfram|Alpha is “to take expert-level knowledge, and create a system that can apply it automatically whenever and wherever it’s needed” says Stephen Wolfram, the technologies inventor (Wolfram, 2009-02). This paper examines Wolfram|Alpha in its present form. Introduction As the internet became available to the world mass population, British computer scientist Tim Berners-Lee provided “hypertext” as a means for its general consumption, and coined the phrase World Wide Web. The World Wide Web is often referred to simply as the Web, and Web 1.0 transformed how we communicate. Now, with Web 2.0 firmly entrenched in our being and going with us wherever we go, can 3.0 be far behind? Web 3.0, the semantic web, is a web that endeavors to understand meaning rather than syntactically precise commands (Andersen, 2010). Enter Wolfram|Alpha. Wolfram Alpha, officially launched in May 2009, is a rapidly evolving "computational search engine,” but rather than searching pre‐existing documents, it actually computes the answer, every time (Andersen, 2010). Wolfram|Alpha relies on a knowledgebase of data in order to perform these computations, which despite efforts to date, is still only a fraction of world’s knowledge. Scientist, author, and inventor Stephen Wolfram refers to the world’s knowledge this way: “It’s a sad but true fact that most data that’s generated or collected, even with considerable effort, never gets any kind of serious analysis” (Wolfram, 2009-02). -
GNU Octave a High-Level Interactive Language for Numerical Computations Edition 3 for Octave Version 2.0.13 February 1997
GNU Octave A high-level interactive language for numerical computations Edition 3 for Octave version 2.0.13 February 1997 John W. Eaton Published by Network Theory Limited. 15 Royal Park Clifton Bristol BS8 3AL United Kingdom Email: [email protected] ISBN 0-9541617-2-6 Cover design by David Nicholls. Errata for this book will be available from http://www.network-theory.co.uk/octave/manual/ Copyright c 1996, 1997John W. Eaton. This is the third edition of the Octave documentation, and is consistent with version 2.0.13 of Octave. Permission is granted to make and distribute verbatim copies of this man- ual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the en- tire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the same conditions as for modified versions. Portions of this document have been adapted from the gawk, readline, gcc, and C library manuals, published by the Free Software Foundation, 59 Temple Place—Suite 330, Boston, MA 02111–1307, USA. i Table of Contents Publisher’s Preface ...................... 1 Author’s Preface ........................ 3 Acknowledgements ........................................ 3 How You Can Contribute to Octave ........................ 5 Distribution .............................................. 6 1 A Brief Introduction to Octave ....... 7 1.1 Running Octave...................................... 7 1.2 Simple Examples ..................................... 7 Creating a Matrix ................................. 7 Matrix Arithmetic ................................. 8 Solving Linear Equations.......................... -
Stan Modeling Language
Stan Modeling Language User’s Guide and Reference Manual Stan Development Team Stan Version 2.12.0 Tuesday 6th September, 2016 mc-stan.org Stan Development Team (2016) Stan Modeling Language: User’s Guide and Reference Manual. Version 2.12.0. Copyright © 2011–2016, Stan Development Team. This document is distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). For full details, see https://creativecommons.org/licenses/by/4.0/legalcode The Stan logo is distributed under the Creative Commons Attribution- NoDerivatives 4.0 International License (CC BY-ND 4.0). For full details, see https://creativecommons.org/licenses/by-nd/4.0/legalcode Stan Development Team Currently Active Developers This is the list of current developers in order of joining the development team (see the next section for former development team members). • Andrew Gelman (Columbia University) chief of staff, chief of marketing, chief of fundraising, chief of modeling, max marginal likelihood, expectation propagation, posterior analysis, RStan, RStanARM, Stan • Bob Carpenter (Columbia University) language design, parsing, code generation, autodiff, templating, ODEs, probability functions, con- straint transforms, manual, web design / maintenance, fundraising, support, training, Stan, Stan Math, CmdStan • Matt Hoffman (Adobe Creative Technologies Lab) NUTS, adaptation, autodiff, memory management, (re)parameterization, optimization C++ • Daniel Lee (Columbia University) chief of engineering, CmdStan, builds, continuous integration, testing, templates,