Probabilistic Programming
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Foundations Of Probabilistic Programming
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Author : Gilles Barthe
language : en
Publisher: Cambridge University Press
Release Date : 2020-12-03
Foundations Of Probabilistic Programming written by Gilles Barthe and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-03 with Computers categories.
This book provides an overview of the theoretical underpinnings of modern probabilistic programming and presents applications in e.g., machine learning, security, and approximate computing. Comprehensive survey chapters make the material accessible to graduate students and non-experts. This title is also available as Open Access on Cambridge Core.
Practical Probabilistic Programming
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Author : Avi Pfeffer
language : en
Publisher: Simon and Schuster
Release Date : 2016-03-29
Practical Probabilistic Programming written by Avi Pfeffer and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-29 with Computers categories.
Summary Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In it, you'll learn how to use the PP paradigm to model application domains and then express those probabilistic models in code. Although PP can seem abstract, in this book you'll immediately work on practical examples, like using the Figaro language to build a spam filter and applying Bayesian and Markov networks, to diagnose computer system data problems and recover digital images. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The data you accumulate about your customers, products, and website users can help you not only to interpret your past, it can also help you predict your future! Probabilistic programming uses code to draw probabilistic inferences from data. By applying specialized algorithms, your programs assign degrees of probability to conclusions. This means you can forecast future events like sales trends, computer system failures, experimental outcomes, and many other critical concerns. About the Book Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In this book, you’ll immediately work on practical examples like building a spam filter, diagnosing computer system data problems, and recovering digital images. You’ll discover probabilistic inference, where algorithms help make extended predictions about issues like social media usage. Along the way, you’ll learn to use functional-style programming for text analysis, object-oriented models to predict social phenomena like the spread of tweets, and open universe models to gauge real-life social media usage. The book also has chapters on how probabilistic models can help in decision making and modeling of dynamic systems. What's Inside Introduction to probabilistic modeling Writing probabilistic programs in Figaro Building Bayesian networks Predicting product lifecycles Decision-making algorithms About the Reader This book assumes no prior exposure to probabilistic programming. Knowledge of Scala is helpful. About the Author Avi Pfeffer is the principal developer of the Figaro language for probabilistic programming. Table of Contents PART 1 INTRODUCING PROBABILISTIC PROGRAMMING AND FIGARO Probabilistic programming in a nutshell A quick Figaro tutorial Creating a probabilistic programming application PART 2 WRITING PROBABILISTIC PROGRAMS Probabilistic models and probabilistic programs Modeling dependencies with Bayesian and Markov networks Using Scala and Figaro collections to build up models Object-oriented probabilistic modeling Modeling dynamic systems PART 3 INFERENCE The three rules of probabilistic inference Factored inference algorithms Sampling algorithms Solving other inference tasks Dynamic reasoning and parameter learning
Probabilistic Programming
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Author : S. Vajda
language : en
Publisher: Academic Press
Release Date : 2014-07-03
Probabilistic Programming written by S. Vajda and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-03 with Mathematics categories.
Probabilistic Programming discusses a high-level language known as probabilistic programming. This book consists of three chapters. Chapter I deals with “wait-and-see problems that require waiting until an observation is made on the random elements, while Chapter II contains the analysis of decision problems, particularly of so-called two-stage problems. The last chapter focuses on “chance constraints, such as constraints that are not expected to be always satisfied, but only in a proportion of cases or “with given probabilities. This text specifically deliberates the decision regions for optimality, probability distributions, Kall's Theorem, and two-stage programming under uncertainty. The complete problem, active approach, quantile rules, randomized decisions, and nonzero order rules are also covered. This publication is suitable for developers aiming to define and automatically solve probability models.
Foundations Of Probabilistic Logic Programming
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Author : Fabrizio Riguzzi
language : en
Publisher: River Publishers
Release Date : 2018-09-01
Foundations Of Probabilistic Logic Programming written by Fabrizio Riguzzi and has been published by River Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-01 with Computers categories.
Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information. Probabilistic Logic Programming is at the intersection of two wider research fields: the integration of logic and probability and Probabilistic Programming. Logic enables the representation of complex relations among entities while probability theory is useful for model uncertainty over attributes and relations. Combining the two is a very active field of study. Probabilistic Programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for the inference and learning tasks are then provided automatically by the system. Probabilistic Logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds. Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. Foundations of Probabilistic Logic Programming aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online.
Bayesian Methods For Hackers
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Author : Cameron Davidson-Pilon
language : en
Publisher: Addison-Wesley Professional
Release Date : 2015-09-30
Bayesian Methods For Hackers written by Cameron Davidson-Pilon and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-30 with Computers categories.
Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes • Learning the Bayesian “state of mind” and its practical implications • Understanding how computers perform Bayesian inference • Using the PyMC Python library to program Bayesian analyses • Building and debugging models with PyMC • Testing your model’s “goodness of fit” • Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works • Leveraging the power of the “Law of Large Numbers” • Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning • Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes • Selecting appropriate priors and understanding how their influence changes with dataset size • Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough • Using Bayesian inference to improve A/B testing • Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.
Abstraction Refinement And Proof For Probabilistic Systems
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Author : Annabelle McIver
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-10-27
Abstraction Refinement And Proof For Probabilistic Systems written by Annabelle McIver and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-10-27 with Computers categories.
Illustrates by example the typical steps necessary in computer science to build a mathematical model of any programming paradigm . Presents results of a large and integrated body of research in the area of 'quantitative' program logics.
Exploiting Program Structure For Scaling Probabilistic Programming
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Author : Steven Holtzen
language : en
Publisher:
Release Date : 2021
Exploiting Program Structure For Scaling Probabilistic Programming written by Steven Holtzen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Probabilistic modeling and reasoning are central tasks in artificial intelligence and machine learning. A probabilistic model is a rough description of the world: the model-builder attempts to capture as much detail about the world's complexities as she can, and when no more detail can be given the rest is left as probabilistic uncertainty. Once constructed, the goal of a model is to perform automated inference: compute the probability that some particular fact is true about the world. It is natural for the model-builder to want a flexible expressive language - the world is a complex thing to describe - and over time this has led to a trend of increasingly powerful modeling languages. This trend is taken to its apex by probabilistic programming languages (PPLs), which enable modelers to specify probabilistic models using the facilities of a full programming language. However, this expressivity comes at a cost: the computational cost of inference is in direct tension with the flexibility of the modeling language, and so it becomes increasingly difficult to design automated inference algorithms that scale to the kinds of systems that model builders want to create. This thesis focuses on the central question: how can we design effective probabilistic programming languages that profitably trade-off expressivity and tractability for inference? The approach taken here is first to identify and exploit important structure that a probabilistic program may possess. The kinds of structure considered here are discrete program structure and symmetry. Programs are heterogeneous objects, so different parts of programs may exhibit different kinds of structure; in the second part of the thesis I show how to decompose heterogeneous probabilistic program inference using a notion of program abstraction. These contributions enable new applications of probabilistic programs in domains such as text analysis, verification of probabilistic systems, and classical simulation of quantum algorithms.
Bayesian Programming
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Author : Pierre Bessiere
language : en
Publisher: CRC Press
Release Date : 2013-12-20
Bayesian Programming written by Pierre Bessiere and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-20 with Business & Economics categories.
Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in natur
Computability Inference And Modeling In Probabilistic Programming
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Author : Daniel Murphy Roy
language : en
Publisher:
Release Date : 2011
Computability Inference And Modeling In Probabilistic Programming written by Daniel Murphy Roy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
We investigate the class of computable probability distributions and explore the fundamental limitations of using this class to describe and compute conditional distributions. In addition to proving the existence of noncomputable conditional distributions, and thus ruling out the possibility of generic probabilistic inference algorithms (even inefficient ones), we highlight some positive results showing that posterior inference is possible in the presence of additional structure like exchangeability and noise, both of which are common in Bayesian hierarchical modeling. This theoretical work bears on the development of probabilistic programming languages (which enable the specification of complex probabilistic models) and their implementations (which can be used to perform Bayesian reasoning). The probabilistic programming approach is particularly well suited for defining infinite-dimensional, recursively-defined stochastic processes of the sort used in nonparametric Bayesian statistics. We present a new construction of the Mondrian process as a partition-valued Markov process in continuous time, which can be viewed as placing a distribution on an infinite kd-tree data structure.
Probabilistic Inductive Logic Programming
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Author : Luc De Raedt
language : en
Publisher: Springer
Release Date : 2008-02-26
Probabilistic Inductive Logic Programming written by Luc De Raedt and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-02-26 with Computers categories.
This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory.