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Practical Probabilistic Programming

Practical Probabilistic Programming Author Avi Pfeffer
ISBN-10 1617292338
Release 2016-04-07
Pages 456
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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



Practical Aspects of Declarative Languages

Practical Aspects of Declarative Languages Author Francesco Calimeri
ISBN-10 9783319733050
Release 2018-01-26
Pages 203
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This book constitutes the proceedings of the 20th International Symposium on Practical Aspects of Declarative Languages, PADL 2018, held in Los Angeles, CA, USA, in January 2018 and collocated with the 45th ACM SIGPLAN Symposium on Principles of Programming Languages.The 13 regular papers presented in this volume together with the abstracts of 2 invited talks were carefully reviewed and selected from 23 submissions. They deal with functional programming; constraint programming and business rules; prolog and optimization; and answer set programming.



Inductive Logic Programming

Inductive Logic Programming Author Paolo Frasconi
ISBN-10 9783642212949
Release 2011-05-23
Pages 278
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This book constitutes the thoroughly refereed post-proceedings of the 20th International Conference on Inductive Logic Programming, ILP 2010, held in Florence, Italy in June 2010. The 11 revised full papers and 15 revised short papers presented together with abstracts of three invited talks were carefully reviewed and selected during two rounds of refereeing and revision. All current issues in inductive logic programming, i.e. in logic programming for machine learning are addressed, in particular statistical learning and other probabilistic approaches to machine learning are reflected.



AI IA 2016 Advances in Artificial Intelligence

AI IA 2016 Advances in Artificial Intelligence Author Giovanni Adorni
ISBN-10 9783319491301
Release 2016-11-24
Pages 554
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This book constitutes the refereed proceedings of the 15th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2016, held in Genova, Italy, in November/December 2016. The 39 full papers presented were carefully reviewed and selected from 53 submissions. The papers are organized in topical sections on optimization and evolutionary algorithms; classification, pattern recognition, and computer vision; multi-agent systems; machine learning; semantic web and description logics; natural language processing; planning and scheduling; and formal verification.



Scalable Uncertainty Management

Scalable Uncertainty Management Author Serafín Moral
ISBN-10 9783319675824
Release 2017-10-19
Pages 438
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This book constitutes the refereed proceedings of the 11th International Conference on Scalable Uncertainty Management, SUM 2017, which was held in Granada, Spain, in October 2017. The 24 full and 6 short papers presented in this volume were carefully reviewed and selected from 35 submissions. The book also contains 3 invited papers. Managing uncertainty and inconsistency has been extensively explored in Artificial Intelligence over a number of years. Now, with the advent of massive amounts of data and knowledge from distributed, heterogeneous, and potentially conflicting sources, there is interest in developing and applying formalisms for uncertainty and inconsistency in systems that need to better manage this data and knowledge. The International Conference on Scalable Uncertainty (SUM) aims to provide a forum for researchers who are working on uncertainty management, in different communities and with different uncertainty models, to meet and exchange ideas.



Bayesian Methods for Hackers

Bayesian Methods for Hackers Author Cameron Davidson-Pilon
ISBN-10 9780133902921
Release 2015-09-30
Pages 256
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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.



Practical Aspects of Declarative Languages

Practical Aspects of Declarative Languages Author Manuel Hermenegildo
ISBN-10 3540243623
Release 2005-01-14
Pages 272
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Practical Aspects of Declarative Languages has been writing in one form or another for most of life. You can find so many inspiration from Practical Aspects of Declarative Languages also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Practical Aspects of Declarative Languages book for free.



Probabilistic Data Analysis with Probabilistic Programming

Probabilistic Data Analysis with Probabilistic Programming Author Feras Ahmad Khaled Saad
ISBN-10 OCLC:1018309791
Release 2016
Pages 50
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Probabilistic techniques are central to data analysis, but dierent approaches can be challenging to apply, combine, and compare. This thesis 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 Bayesian models, multivariate kernel methods, discriminative machine learning, clustering algorithms, dimensionality reduction, and arbitrary probabilistic programs. We also demonstrate the integration of CGPMs into BayesDB, a probabilistic programming platform that can express data analysis tasks using a modeling language and a structured query language. The practical value is illustrated in two ways. First, CGPMs are used in an analysis that identifies satellite data records which probably violate Kepler’s Third Law, by composing causal probabilistic programs with non-parametric Bayes in under 50 lines of probabilistic code. Second, for several representative data analysis tasks, we report on lines of code and accuracy measurements of various CGPMs, plus comparisons with standard baseline solutions from Python and MATLAB libraries.



Logic for Programming Artificial Intelligence and Reasoning

Logic for Programming  Artificial Intelligence  and Reasoning Author Ken McMillan
ISBN-10 9783642452215
Release 2013-12-05
Pages 794
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This book constitutes the proceedings of the 19th International Conference on Logic for Programming, Artificial Intelligence and Reasoning, LPAR-19, held in December 2013 in Stellenbosch, South Africa. The 44 regular papers and 8 tool descriptions and experimental papers included in this volume were carefully reviewed and selected from 152 submissions. The series of International Conferences on Logic for Programming, Artificial Intelligence and Reasoning (LPAR) is a forum where year after year, some of the most renowned researchers in the areas of logic, automated reasoning, computational logic, programming languages and their applications come to present cutting-edge results, to discuss advances in these fields and to exchange ideas in a scientifically emerging part of the world.



Probabilistic constraint logic programming

Probabilistic constraint logic programming Author Stefan Riezler
ISBN-10 STANFORD:36105112485375
Release 1999
Pages 142
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Probabilistic constraint logic programming has been writing in one form or another for most of life. You can find so many inspiration from Probabilistic constraint logic programming also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Probabilistic constraint logic programming book for free.



Inductive Logic Programming

Inductive Logic Programming Author Stephen Muggleton
ISBN-10 9783540738466
Release 2007-07-27
Pages 456
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This book constitutes the thoroughly refereed post-proceedings of the 16th International Conference on Inductive Logic Programming, ILP 2006, held in Santiago de Compostela, Spain, in August 2006.The 27 revised full papers presented together with 5 invited papers and the extended abstracts of 7 special issue papers were carefully reviewed and selected from 77 initial submissions. The papers address all current topics in inductive logic programming, ranging from theoretical and methodological issues to advanced applications in various areas, thus presenting original results on all aspects of learning in logic, as well as multi-relational data mining and learning, statistical relational learning, graph and tree mining, and learning in other (non-propositional) logic-based knowledge representation frameworks.



Principles and Practice of Constraint Programming CP 2007

Principles and Practice of Constraint Programming   CP 2007 Author Christian Bessiere
ISBN-10 9783540749707
Release 2007-10-11
Pages 887
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This book constitutes the refereed proceedings of the 13th International Conference on Principles and Practice of Constraint Programming, CP 2007. It contains 51 revised full papers and 14 revised short papers presented together with eight application papers and the abstracts of two invited lectures. All current issues of computing with constraints are addressed, ranging from methodological and foundational aspects to solving real-world problems in various application fields.



Building Probabilistic Graphical Models with Python

Building Probabilistic Graphical Models with Python Author Kiran R Karkera
ISBN-10 9781783289011
Release 2014-06-25
Pages 172
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This is a short, practical guide that allows data scientists to understand the concepts of Graphical models and enables them to try them out using small Python code snippets, without being too mathematically complicated. If you are a data scientist who knows about machine learning and want to enhance your knowledge of graphical models, such as Bayes network, in order to use them to solve real-world problems using Python libraries, this book is for you.This book is intended for those who have some Python and machine learning experience, or are exploring the machine learning field.



Probabilistic and Randomized Methods for Design under Uncertainty

Probabilistic and Randomized Methods for Design under Uncertainty Author Giuseppe Calafiore
ISBN-10 9781846280955
Release 2006-03-06
Pages 458
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Probabilistic and Randomized Methods for Design under Uncertainty is a collection of contributions from the world’s leading experts in a fast-emerging branch of control engineering and operations research. The book will be bought by university researchers and lecturers along with graduate students in control engineering and operational research.



Practical Reliability Engineering and Analysis for System Design and Life Cycle Sustainment

Practical Reliability Engineering and Analysis for System Design and Life Cycle Sustainment Author William Wessels
ISBN-10 9781420094404
Release 2010-04-16
Pages 497
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In today’s sophisticated world, reliability stands as the ultimate arbiter of quality. An understanding of reliability and the ultimate compromise of failure is essential for determining the value of most modern products and absolutely critical to others, large or small. Whether lives are dependent on the performance of a heat shield or a chip in a lab, random failure is never an acceptable outcome. Written for practicing engineers, Practical Reliability Engineering and Analysis for System Design and Life-Cycle Sustainment departs from the mainstream approach for time to failure-based reliability engineering and analysis. The book employs a far more analytical approach than those textbooks that rely on exponential probability distribution to characterize failure. Instead, the author, who has been a reliability engineer since 1970, focuses on those probability distributions that more accurately describe the true behavior of failure. He emphasizes failure that results from wear, while considering systems, the individual components within those systems, and the environmental forces exerted on them. Dependable Products Are No Accident: A Clear Path to the Creation of Consistently Reliable Products Taking a step-by-step approach that is augmented with current tables to configure wear, load, distribution, and other essential factors, this book explores design elements required for reliability and dependable systems integration and sustainment. It then discusses failure mechanisms, modes, and effects—as well as operator awareness and participation—and also delves into reliability failure modeling based on time-to-failure data considering a variety of approaches. From there, the text demonstrates and then considers the advantages and disadvantages for the stress-strength analysis approach, including various phases of test simulation. Taking the practical approach still further, the author covers reliability-centered failure analysis, as well as condition-based and time-directed maintenance. As a science, reliability was once considered the plaything of statisticians reporting on time-to-failure measurements, but in the hands of a practicing engineer, reliability is much more than the measure of an outcome; it is something to be achieved, something to quite purposely build into a system. Reliability analysis of mechanical design for structures and dynamic components demands a thorough field-seasoned approach that first looks to understand why a part fails, then learns how to fix it, and finally learns how to prevent its failing. Ultimately, reliability of mechanical design is based on the relationship between stress and strength over time. This book blends the common sense of lessons learned with mechanical engineering design and systems integration, with an eye toward sustainment. This is the stuff that enables organizations to achieve products valued for their world-class reliability.



Bayesian Analysis with Python

Bayesian Analysis with Python Author Osvaldo Martin
ISBN-10 9781785889851
Release 2016-11-25
Pages 282
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Unleash the power and flexibility of the Bayesian framework About This Book Simplify the Bayes process for solving complex statistical problems using Python; Tutorial guide that will take the you through the journey of Bayesian analysis with the help of sample problems and practice exercises; Learn how and when to use Bayesian analysis in your applications with this guide. Who This Book Is For Students, researchers and data scientists who wish to learn Bayesian data analysis with Python and implement probabilistic models in their day to day projects. Programming experience with Python is essential. No previous statistical knowledge is assumed. What You Will Learn Understand the essentials Bayesian concepts from a practical point of view Learn how to build probabilistic models using the Python library PyMC3 Acquire the skills to sanity-check your models and modify them if necessary Add structure to your models and get the advantages of hierarchical models Find out how different models can be used to answer different data analysis questions When in doubt, learn to choose between alternative models. Predict continuous target outcomes using regression analysis or assign classes using logistic and softmax regression. Learn how to think probabilistically and unleash the power and flexibility of the Bayesian framework In Detail The purpose of this book is to teach the main concepts of Bayesian data analysis. We will learn how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, to check models and validate them. This book begins presenting the key concepts of the Bayesian framework and the main advantages of this approach from a practical point of view. Moving on, we will explore the power and flexibility of generalized linear models and how to adapt them to a wide array of problems, including regression and classification. We will also look into mixture models and clustering data, and we will finish with advanced topics like non-parametrics models and Gaussian processes. With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems. Style and approach Bayes algorithms are widely used in statistics, machine learning, artificial intelligence, and data mining. This will be a practical guide allowing the readers to use Bayesian methods for statistical modelling and analysis using Python.



Probabilistic Constrained Optimization

Probabilistic Constrained Optimization Author Stanislav Uryasev
ISBN-10 0792366441
Release 2000-11-30
Pages 307
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Probabilistic and percentile/quantile functions play an important role in several applications, such as finance (Value-at-Risk), nuclear safety, and the environment. Recently, significant advances have been made in sensitivity analysis and optimization of probabilistic functions, which is the basis for construction of new efficient approaches. This book presents the state of the art in the theory of optimization of probabilistic functions and several engineering and finance applications, including material flow systems, production planning, Value-at-Risk, asset and liability management, and optimal trading strategies for financial derivatives (options). Audience: The book is a valuable source of information for faculty, students, researchers, and practitioners in financial engineering, operation research, optimization, computer science, and related areas.