{"title":"Vate","authors":"D. Goodman, A. Pocock, Jason Peck, Guy L. Steele","doi":"10.1145/3437984.3458835","DOIUrl":null,"url":null,"abstract":"Inspired by earlier work on Augur, Vate is a probabilistic programming language for the construction of JVM based probabilistic models with an Object-Oriented interface. As a compiled language it is able to examine the dependency graph of the model to produce optimised code that can be dynamically targeted to different platforms. Using Gibbs Sampling, Metropolis-Hastings and variable marginalisation it can handle a range of model types and is able to efficiently infer values, estimate probabilities, and execute models.","PeriodicalId":269840,"journal":{"name":"Proceedings of the 1st Workshop on Machine Learning and Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 1st Workshop on Machine Learning and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3437984.3458835","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Inspired by earlier work on Augur, Vate is a probabilistic programming language for the construction of JVM based probabilistic models with an Object-Oriented interface. As a compiled language it is able to examine the dependency graph of the model to produce optimised code that can be dynamically targeted to different platforms. Using Gibbs Sampling, Metropolis-Hastings and variable marginalisation it can handle a range of model types and is able to efficiently infer values, estimate probabilities, and execute models.