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Bayesian Methods f08r Hackers Probabilistic Programming 4nd Bayesian Inference

Release: Bayesian.Methods.f08r.Hackers.Probabilistic.Programming.4nd.Bayesian.Inference

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Bayesian Methods f08r Hackers: Probabilistic Programming 4nd Bayesian Inference

Bayesian Methods f08r Hackers: Probabilistic Programming 4nd Bayesian Inference by Cameron Davidson-Pilon
English | 2 Oct. 2015 | ISBN: 0133902838 | 250 Pages | PDF (True) | 19.4 MB
Master Bayesian Inference through Practical Examples 4nd Computation–Without Advanced Mathematical Analysis

Bayesian methods of inference are deeply natural 4nd extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses 4nd 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 the08ry to practice–freeing you to get results using computing power.

Bayesian Methods f08r Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language 4nd the closely related Python tools NumPy, SciPy, 4nd 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 4nd guiding you through building 4nd training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples 4nd intuitive explanations that have been refined after extensive user feedback. Youll learn how to use the Markov Chain Monte Carlo alg08rithm, choose appropriate sample sizes 4nd pri08rs, w08rk with loss functions, 4nd apply Bayesian inference in domains ranging from finance to marketing. Once youve mastered these techniques, youll constantly turn to this guide f08r the w08rking PyMC code you need to jumpstart future projects.

Coverage includes

Learning the Bayesian state of mind 4nd its practical implications
Underst4nding how computers perf08rm Bayesian inference
Using the PyMC Python library to program Bayesian analyses
Building 4nd debugging models with PyMC
Testing your models goodness of fit
Opening the black box of the Markov Chain Monte Carlo alg08rithm to see how 4nd why it w08rks
Leveraging the power of the Law of Large Numbers
Mastering key concepts, such as clustering, convergence, autoc08rrelation, 4nd thinning
Using loss functions to measure an estimates weaknesses based on your goals 4nd desired outcomes
Selecting appropriate pri08rs 4nd underst4nding how their influence changes with data5eut size
Overcoming the expl08ration 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 w08rked in many areas of applied mathematics, from the evolutionary dynamics of genes 4nd 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 4nd at the Independent University of Moscow, he currently w08rks with the online commerce leader Shopify.

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