probcomp.csail.mit.edu

The MIT Probabilistic Computing Project

The MIT Probabilistic Computing Project. So far, this work has yielded new general-purpose probabilistic programming technology and intentionally stochastic (but still digital) hardware for real-time Bayesian inference. It has also yielded academic and commercial Bayesian database systems that automate the analysis of high-dimensional data tables. An interactive, Turing-complete probabilistic programming platform descended from Church. Taylor Campbell (Research Engineer). PhD Student; starting Fall 2015).

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The MIT Probabilistic Computing Project | probcomp.csail.mit.edu Reviews

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The MIT Probabilistic Computing Project. So far, this work has yielded new general-purpose probabilistic programming technology and intentionally stochastic (but still digital) hardware for real-time Bayesian inference. It has also yielded academic and commercial Bayesian database systems that automate the analysis of high-dimensional data tables. An interactive, Turing-complete probabilistic programming platform descended from Church. Taylor Campbell (Research Engineer). PhD Student; starting Fall 2015).

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The MIT Probabilistic Computing Project | probcomp.csail.mit.edu Reviews

https://probcomp.csail.mit.edu

The MIT Probabilistic Computing Project. So far, this work has yielded new general-purpose probabilistic programming technology and intentionally stochastic (but still digital) hardware for real-time Bayesian inference. It has also yielded academic and commercial Bayesian database systems that automate the analysis of high-dimensional data tables. An interactive, Turing-complete probabilistic programming platform descended from Church. Taylor Campbell (Research Engineer). PhD Student; starting Fall 2015).

INTERNAL PAGES

probcomp.csail.mit.edu

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Welcome to CrossCat

http://probcomp.csail.mit.edu/crosscat

A fully Bayesian nonparametric method for analyzing heterogenous, high dimensional data. CrossCat Developer Alpha 0.1.0 Released! A range of exploratory analysis and predictive modeling tasks can be addressed via CrossCat, including detecting predictive relationships between variables, finding multiple overlapping clusterings, imputing missing values, and simultaneously selecting features and classifying rows. Research on CrossCat has shown that it is suitable for analysis of real-world tables of up ...

2

The MIT Probabilistic Computing Project

http://probcomp.csail.mit.edu/index.html

The MIT Probabilistic Computing Project. The MIT Probabilistic Computing Project aims to build software and hardware systems that augment human and machine intelligence. We are currently focused on probabilistic programming. Probabilistic programming is an emerging field that draws on probability theory, programming languages, and systems programming to provide concise, expressive languages for modeling and general-purpose inference engines that both humans and machines can use. Convening in Seattle, org...

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Landing Page – Layout Examples – Pure

http://probcomp.csail.mit.edu/gpgp

Generative Probabilistic Graphics Programming. Probablistic program that infers simple 3D road models. Road Scene Parsing Examples. GPGP for Road Scenes. Caltech Baseline for Road Scenes. MIT Probabilistic Computing and Computational Cognitive Sciences Group. Template borrowed from YUI Team and purecss.io.

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Venture -- a general-purpose probabilistic programming platform

http://probcomp.csail.mit.edu/venture

Venture Developer Alpha 0.5 Released! Venture is a prototype general-purpose probabilistic computing platform. Venture hosts our (and in principle third-party) probabilistic applications, such as BayesDB. Venture is programmed primarily in VentureScript, but also supports applications written in other probabilistic or traditional programming languages. Venture is rapidly evolving, alpha-quality. Sufficient expressiveness to handle problems and data sources from multiple fields, such as machine learning, ...

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BayesDB - Query the probable implicatons of your data - The MIT Probabilistic Computing Project

http://probcomp.csail.mit.edu/bayesdb

Is it possible to make statistical inference broadly accessible to non-statisticians without sacrificing mathematical rigor or inference quality? INFER orbit type FROM satellites. WITH CONFIDENCE 0.7. BayesDB is a probabilistic programming platform that enables users to query the probable implications of their data as directly as SQL databases enable them to query the data itself. Bayesian Query Language (BQL). The Bayesian Query Lanuage (BQL) allows analysts and domain experts to interact perform Bayesi...

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blog.probcomp.org

Vikash Mansinghka | MIT Probabilistic Computing Project

http://blog.probcomp.org/author/vkm

MIT Probabilistic Computing Project. All posts by Vikash Mansinghka. September 19, 2015. Welcome to the research blog for the MIT Probabilistic Computing Project. We’ll be using this blog to:. Announce research projects, software releases, data analyses, and academic publications. Present mini tutorials on technical topics, especially probabilistic programming. Illustrate connections between probabilistic computing and work in other fields, especially the social sciences and artificial intelligence.

blog.probcomp.org

September | 2015 | MIT Probabilistic Computing Project

http://blog.probcomp.org/2015/09

MIT Probabilistic Computing Project. Monthly Archives: September 2015. Release Announcement: Bayeslite 0.1. September 22, 2015. The MIT Probabilistic Computing Project. Is pleased to announce a generally available pre-release of bayeslite, a new implementation of BayesDB. For installation instructions, documentation, and to provide us with feedback, please visit http:/ probcomp.csail.mit.edu/bayesdb. Bayeslite 0.1 provides a bare-bones implementation of the Bayesian Query Language (BQL), including:.

blog.probcomp.org

Uncategorized | MIT Probabilistic Computing Project

http://blog.probcomp.org/category/uncategorized

MIT Probabilistic Computing Project. Release Announcement: Bayeslite 0.1. September 22, 2015. The MIT Probabilistic Computing Project. Is pleased to announce a generally available pre-release of bayeslite, a new implementation of BayesDB. For installation instructions, documentation, and to provide us with feedback, please visit http:/ probcomp.csail.mit.edu/bayesdb. Bayeslite 0.1 provides a bare-bones implementation of the Bayesian Query Language (BQL), including:. PROBABILITY DENSITY OF col. Union of C...

blog.probcomp.org

Release Announcement: Bayeslite 0.1 | MIT Probabilistic Computing Project

http://blog.probcomp.org/2015/09/22/release-bayeslite-0-1

MIT Probabilistic Computing Project. Release Announcement: Bayeslite 0.1. September 22, 2015. The MIT Probabilistic Computing Project. Is pleased to announce a generally available pre-release of bayeslite, a new implementation of BayesDB. For installation instructions, documentation, and to provide us with feedback, please visit http:/ probcomp.csail.mit.edu/bayesdb. Bayeslite 0.1 provides a bare-bones implementation of the Bayesian Query Language (BQL), including:. PROBABILITY DENSITY OF col. Union of C...

blog.probcomp.org

First Post | MIT Probabilistic Computing Project

http://blog.probcomp.org/2015/09/19/first-post

MIT Probabilistic Computing Project. September 19, 2015. Welcome to the research blog for the MIT Probabilistic Computing Project. We’ll be using this blog to:. Announce research projects, software releases, data analyses, and academic publications. Present mini tutorials on technical topics, especially probabilistic programming. Illustrate connections between probabilistic computing and work in other fields, especially the social sciences and artificial intelligence. Leave a Reply Cancel reply.

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About Richard Tibbetts @ Hyperextended Metaphor

http://innocuous.org/tibbetts

Richard Tibbetts on Various Topics. I’m currently Visiting Scientist at the MIT Probabilistic Computing Project. Where I lead the open source project for BayesDB. And help with other probabilistic computing systems like Venture. Enables people to query the implications of their data without requiring deep analytic expertise. Prior to MIT from 2013 to 2015 I was Chief Technology Officer for Event Processing at TIBCO Software. Following the successful merger with StreamBase. Part of the Aurora Project.

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The MIT Probabilistic Computing Project

The MIT Probabilistic Computing Project. So far, this work has yielded new general-purpose probabilistic programming technology and intentionally stochastic (but still digital) hardware for real-time Bayesian inference. It has also yielded academic and commercial Bayesian database systems that automate the analysis of high-dimensional data tables. An interactive, Turing-complete probabilistic programming platform descended from Church. Taylor Campbell (Research Engineer). PhD Student; starting Fall 2015).

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