Davidson Cameron (103 results)

Hardcover. Condition: Very Good. 1st UK. With dust jacket. 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.

Condition: Fine. Used book that is in almost brand-new condition. May contain a remainder mark. Better World Books: Buy Books. Do Good.

Condition: Very Good. 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.

Condition: Very Good. Very Good condition. Good dust jacket. A copy that may have a few cosmetic defects. May also contain light spine creasing or a few markings such as an owner's name, short gifter's inscription or light stamp.

Condition: Very Good. Very Good condition. Acceptable dust jacket. A copy that may have a few cosmetic defects. May also contain light spine creasing or a few markings such as an owner's name, short gifter's inscription or light stamp.

Condition: Very Good. Very Good Condition. Has some wear. Five star seller - Buy with confidence.

Condition: very_good. Fast Free Shipping â" Very Good condition book with a firm cover and clean pages. Shows normal use and some light wear or limited notes markings. A solid, nice copy to enjoy.

spiral_bound. Condition: Good. This is a used book in good condition and may show some signs of use or wear . This is a used book in good condition and may show some signs of use or wear .

hardcover. Condition: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

hardcover. Condition: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

Spiral-bound. Condition: 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.

Hardback. Condition: Good.

Spiral-bound. Condition: Acceptable. Connecting readers with great books since 1972. Used textbooks may not include companion materials such as access codes, etc. May have condition issues including wear and notes/highlighting. We ship orders daily and Customer Service is our top priority.

Spiral-bound. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Hardback. Condition: Good. Cameron Davidson (illustrator).

Condition: Very Good.

Condition: Very Good.

hardcover. Condition: Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

Condition: Very Good. Limited Copies Available - Very Good Condition - May have school stamp and/or student names - May have some cover wear - DOES NOT INCLUDE ANY CDs OR ACCESS CODES IF APPLICABLE.

Hardback. Condition: Good.

Hardback. Condition: Good. Cameron Davidson (illustrator).

Hardcover. Condition: Very Good. Dust Jacket Condition: Very Good. Please note the image in this listing is a stock photo and may not match the covers of the actual item. Book.

Hardcover. Condition: Very Good. Near Fine. In Washington, D.C. From Above the aerial photography of Cameron Davidson gives you the opportunity to enjoy images of the capital city's most distinctive high-rise buildings and attractions as well as its lesser-known but fascinating landmarks. Please note the image in this listing is a stock photo and may not match the covers of the actual item. Book.…

Condition: Very Good. Very Good condition. Very Good dust jacket. A copy that may have a few cosmetic defects. May also contain light spine creasing or a few markings such as an owner's name, short gifter's inscription or light stamp. NOT AVAILABLE FOR SHIPMENT OUTSIDE OF THE UNITED STATES.

Condition: Good. Good condition. Very Good dust jacket. A copy that has been read but remains intact. May contain markings such as bookplates, stamps, limited notes and highlighting, or a few light stains. NOT AVAILABLE FOR SHIPMENT OUTSIDE OF THE UNITED STATES.

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

Paperback. Condition: 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. Condition: 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. Condition: 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.…

Hardback. Condition: Good.