BayesDB: A probabilistic programming system for querying the probable implications of data
نویسندگان
چکیده
Is it possible to make statistical inference broadly accessible to non-statisticians without sacrificing mathematical rigor or inference quality? This paper describes BayesDB, a probabilistic programming platform that aims to enable users to query the probable implications of their data as directly as SQL databases enable them to query the data itself. This paper focuses on four aspects of BayesDB: (i) BQL, an SQL-like query language for Bayesian data analysis, that answers queries by averaging over an implicit space of probabilistic models; (ii) techniques for implementing BQL using a broad class of multivariate probabilistic models; (iii) a semi-parametric Bayesian model-builder that auomatically builds ensembles of factorial mixture models to serve as baselines; and (iv) MML, a “meta-modeling” language for imposing qualitative constraints on the model-builder and combining baseline models with custom algorithmic and statistical models that can be implemented in external software. BayesDB is illustrated using three applications: cleaning and exploring a public database of Earth satellites; assessing the evidence for temporal dependence between macroeconomic indicators; and analyzing a salary survey.
منابع مشابه
BayesDB: Querying the Probable Implications
BayesDB, a Bayesian database table, lets users query the probable implications of their tabular data as easily as an SQL database lets them query the data itself. Using the built-in Bayesian Query Language (BQL), users with little statistics knowledge can solve basic data science problems, such as detecting predictive relationships between variables, inferring missing values, simulating probabl...
متن کاملWhat is the Population of Interest: Population Modeling for BayesDB
BayesDB [1, 2] is a probabilistic programming platform that enables users to solve probabilistic data analysis problems using a simple, SQL-like language. Queries execute against generative population models (GPMs), a new abstraction that can be used to integrate data, metadata, qualitative domain knowledge, and quantitative models. Baseline quantitative models are typically built via an AI mod...
متن کاملProbabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes
Databases are widespread, yet extracting relevant data can be difficult. Without substantial domain knowledge, multivariate search queries often return sparse or uninformative results. This paper introduces an approach for searching structured data based on probabilistic programming and nonparametric Bayes. Users specify queries in a probabilistic language that combines standard SQL database se...
متن کاملA Probabilistic Programming Approach To Probabilistic Data Analysis
Probabilistic techniques are central to data analysis, but different approaches can be challenging to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include discriminative...
متن کاملProbabilistic Data Analysis with Probabilistic Programming
Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bay...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1512.05006 شماره
صفحات -
تاریخ انتشار 2015