Bayesian inference (421 Ergebnisse)

Paperback. Zustand: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Paperback. Zustand: Fair. Designed to form the basis of a graduate course on Bayesian inference, this textbook discusses important general issues of the Bayesian approach. It investigates problems, illustrating the appropriate analysis of mathematical results with numerical examples.

Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Paperback. Zustand: Good. Statisticians now generally acknowledge the theorectical importance of Bayesian inference, if not its practical validity. According to Gudmund R. Iversen, one reason for the lag in applications is that empirical researchers have lacked a grounding in the methodology. His volume provides this introduction and serves as a companion to #4, Tests of Significance.…

Softcover. Zustand: Bon. Ancien livre de bibliothèque. Petite(s) trace(s) de pliure sur la couverture. Edition 1999. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Slightly creased cover. Edition 1999. Ammareal gives back up to 15% of this item's net price to charity organizations. …

Paperback. Zustand: Very Good. 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.…

Paperback. Zustand: Good. 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.…

Zustand: Very Good. 1 Edition. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Zustand: Good. 1 Edition. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Softcover. Zustand: Bon. Ancien livre de bibliothèque. Livre un peu vrillé. Petite(s) trace(s) de pliure sur la couverture. Légères traces d'usure sur la couverture. Salissures sur la tranche. Traces d'humidité sur les pages. Traces d'humidité sur les premières et dernières pages ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Book slightly twisted. Slightly creased cover. Slight signs of wear on the cover. Stains on the edge. Traces of humidity on the pages. Traces of humidity on the first and last pages. Edition 1999. Ammareal gives back up to 15% of this.…

Zustand: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Zustand: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Hardback. Zustand: Good. Bridging the gap between research and application, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference provides a concise, and integrated account of Markov chain Monte Carlo (MCMC) for performing Bayesian inference. This volume, which was developed from a short course taught by the author at a meeting of Brazilian statisticians and probabilists, retains the didactic character of the original course text. The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. It describes each component of the theory in detail and outlines related software, which is of particular benefit to applied scientists.…

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Heft. Zustand: Gut. Seiten 164-174 Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung insgesamt sauber. Es befindet sich lediglich ein Bibliotheksstempel im Heft; ordnungsgemäß entwidmet. Englische Sprache. Sprache: Englisch Gewicht in Gramm: 20.

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Zustand: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

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Paperback. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

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Paperback. Zustand: Very Good. IVERSEN: BAYESIAN STATISTICAL INFERENCE (PAPER) (Quantitative Applications in the Social Sciences) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping.…

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