Bayesian inference (Mehr als 1.500 Ergebnisse)

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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.

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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.…

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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.…

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.

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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.…

Trade paperback. Wiley Classics Lib ed. Trade paperback (US). Glued binding. 608 p. Contains: Unspecified. Wiley Classics Library, 40. Audience: General/trade. Very good. light shelfwear, previous owner name on first page.

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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.

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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.…

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.…

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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.…

Zustand: New. pp. xiii + 191 Figures.

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Zustand: New. pp. xiii + 191 Index.

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.

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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