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Backpacking Essentials

RRP $16.99

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Backpacking Essentials: A Quick Start Guide to Backpacking Light for Beginners
Picture yourself going backpacking in the Great Smokey Mountains. Got it? What would that look like? Do you imagine yourself with a 100-pound pack on your back? Stooped over under the weight? Exhausted after less than a mile of hiking? That would be WRONG! Backpacking is intended to be fun. This book shows even beginners how to make that happen!

Backpacking Essentials is written to teach beginners how to enter the hiking experience in a way that is safe and fun. You'll have all you need packed neatly into a small backpack that doesn't rob you of the job of the journey. It isn't automatic. You need someone who understands and who is capable of teaching and coaching you along the way. That's whereBackpacking Essentials author Paul Greene enters the picture. He gives simple step-by-step tips to turn beginners into veteran backpackers.

In Backpacking Essentials, you will learn:

• The essential things needed to pack light for your backpacking adventure
• What to take along to deal with minor first aid or safety issues
• The two types of navigation and the navigational aids to take along
• How to select the clothing and boots for this expedition
• What to take along for starting and fire and providing needed light
• How to keep your skin clean and protect it against the elements
• How to have plenty of water (remember, you are backpacking light!)
• What lightweight and nutritious food to keep in your pack
• How to choose and load your backpack
• The benefits and best size of backpacking stick

Bayesian Inference For Probabilistic Risk Assessment

RRP $491.99

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Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing inference on these problems. Inference in the book employs a modern computational approach known as Markov chain Monte Carlo (MCMC). The MCMC approach may be implemented using custom-written routines or existing general purpose commercial or open-source software. This book uses an open-source program called OpenBUGS (commonly referred to as WinBUGS) to solve the inference problems that are described. A powerful feature of OpenBUGS is its automatic selection of an appropriate MCMC sampling scheme for a given problem. The authors provide analysis "building blocks" that can be modified, combined, or used as-is to solve a variety of challenging problems.

The MCMC approach used is implemented via textual scripts similar to a macro-type programming language. Accompanying most scripts is a graphical Bayesian network illustrating the elements of the script and the overall inference problem being solved.Bayesian Inference for Probabilistic Risk Assessment also covers the important topics of MCMC convergence and Bayesian model checking.

Bayesian Inference for Probabilistic Risk Assessment is aimed at scientists and engineers who perform or review risk analyses. It provides an analytical structure for combining data and information from various sources to generate estimates of the parameters of uncertainty distributions used in risk and reliability models.

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