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Bayesian Methods for Hackers - Probabilistic Programming and Bayesian Inference (Paperback)
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Bayesian Methods for Hackers - Probabilistic Programming and Bayesian Inference (Paperback)
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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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