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001 ocn881450744;

003 OCoLC;

005 20210129095605.0;

008 150513s2016 nyua e b 001 0 eng ;

010 a| 2015017249;

019 a| 923499787;

020 a| 9780133902839q| (pbk. ;q| alk. paper);

020 a| 0133902838q| (pbk. ;q| alk. paper);

024 3 a| 9780133902839;

035 a| (Sirsi) 99985532139;

035 a| 99985532139;

035 a| (OCoLC)881450744z| (OCoLC)923499787;

037 b| Prentice Hall, C/O Pearson Education Order Dept 135 S Mount Zion rd, Lebanon, IN, USA, 46052n| SAN 200-2175;

042 a| pcc;

050 00 a| QA76.9.A25b| D376 2016;

082 00 a| 006.32| 23;

084 a| 006.3b| DAV;

100 1 a| Davidson-Pilon, Cameron,e| author.=| ^A1413372;

245 10 a| Bayesian methods for hackers :b| probabilistic programming and Bayesian inference /c| Cameron Davidson-Pilon.;

264 1 a| New York :b| Addison-Wesley,c| [2016];

300 a| xvi, 226 pages :b| illustrations ;c| 24 cm.;

336 a| textb| txt2| rdacontent;

337 a| unmediatedb| n2| rdamedia;

338 a| volumeb| nc2| rdacarrier;

490 1 a| Addison Wesley data & analytics series;

504 a| Includes bibliographical references and index.;

505 0 a| The philosophy of Bayesian inference -- A little more on PyMC -- Opening the black box of MCMC -- The greatest theorem never told -- Would you rather lose an arm or a leg? -- Getting our priorities straight -- Bayesian A/B testing;

520 a| "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."--Amazon.com;

650 0 a| Penetration testing (Computer security)x| Mathematics.=| ^A1157060;

650 0 a| Bayesian statistical decision theory.=| ^A20878;

650 0 a| Soft computing.=| ^A390094;

650 7 a| Entscheidungstheorie.2| gnd;

650 7 a| BAYES.2| gnd;

650 7 a| Soft Computing.2| gnd;

650 7 a| Penetrationstest.2| gnd;

650 7 a| Statistische Entscheidungstheorie.2| gnd;

650 7 a| Bayesian statistical decision theory.2| fast0| (OCoLC)fst00829019;

650 7 a| Soft computing.2| fast0| (OCoLC)fst01124115;

830 0 a| Addison-Wesley data and analytics series.=| ^A1364699;

949 i| 30372017371847o| jjlm;

960 o| 1s| 39.99t| Joyner48u| JETCz| USD;

596 a| 1;

998 a| 5525698;

999 a| QA76.9 .A25 D376 2016w| LCc| 1i| 30372017371847d| 10/25/2023e| 6/22/2023l| JGESm| JOYNERn| 1r| Ys| Yt| JGESBKu| 12/8/2020x| BOOKz| JSTACKSo| .STAFF. jjlm;

Bayesian methods for hackers : probabilistic programming and Bayesian inference / Cameron Davidson-Pilon.

Author/creator	Davidson-Pilon, Cameron author.
Format	Book
Publication	New York : Addison-Wesley, [2016]
Description	xvi, 226 pages : illustrations ; 24 cm.
Subjects	Penetration testing (Computer security)--Mathematics. Bayesian statistical decision theory. Soft computing.

Series	Addison Wesley data & analytics series Addison-Wesley data and analytics series. ^A1364699
Contents	The philosophy of Bayesian inference -- A little more on PyMC -- Opening the black box of MCMC -- The greatest theorem never told -- Would you rather lose an arm or a leg? -- Getting our priorities straight -- Bayesian A/B testing
Abstract	"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."--Amazon.com
Bibliography note	Includes bibliographical references and index.
LCCN	2015017249
ISBN	9780133902839 (pbk. ; alk. paper)
ISBN	0133902838 (pbk. ; alk. paper)
Standard identifier#	9780133902839
Stock number	Prentice Hall, C/O Pearson Education Order Dept 135 S Mount Zion rd, Lebanon, IN, USA, 46052 SAN 200-2175

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Joyner	General Stacks	QA76.9 .A25 D376 2016	✔ Available	Place Hold

Bayesian methods for hackers : probabilistic programming and Bayesian inference / Cameron Davidson-Pilon.

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