Sarah Moss argues that in addition to full beliefs credences can constitute knowledge. She introduces the notion of probabilistic content and shows how it plays a central role not only in epistemology but in the philosophy of mind and language. Just you can believe an... Lees meer.

An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning Bayesian inference generative models and decision making under uncertainty. An advanced counterpart to Probabilistic Machine Learni... Lees meer.

... Lees meer.

This monumental work traces the rise the transformation and the diffusion of probabilistic and statistical thinking in the nineteenth and twentieth centuries. The contributors - scientists historians and philosophers of science from eight countries make it possible... Lees meer.

... Lees meer.

A defining work of Econophysics republished for the first time since 1983 Laws of Chaos is an attempt to construct a non-deterministic theoretical framework for the foundations of political economy. It relies on probabilistic and statistical methods of the kind used... Lees meer.

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today s Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these developing methods that can... Lees meer.

Presents computer methods for analysing DNA RNA and protein sequences. Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example h... Lees meer.

This book presents an overview of the necessary information needed to make educational assumptions about the statistical or probable characteristics of a situation. The book can be used as a supplemental text in courses on probability logic statistics Lack of abi... Lees meer.

Bayesian inference uses probability distributions and Bayes theorem to build flexible models. The book uses PyMC3 to abstract all the mathematical and computational details from this process allowing readers to solve a wide range of problems in data science. Bayesi... Lees meer.

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 an... Lees meer.

... Lees meer.

All scientific disciplines prize predictive success. Conventional statistical analyses however treat prediction as secondary instead focusing on modeling and hence estimation testing and detailed physical interpretation tackling these tasks before the predictive... Lees meer.

This comprehensive book discusses uncertainty modeling of renewable energy resources and its steady state analysis. It also discusses challenges related to renewable energy integration to the grid techniques to mitigate these challenges and protection of power system... Lees meer.

Rigorous probabilistic arguments built on the foundation of measure theory introduced eighty years ago by Kolmogorov have invaded many fields. Students of statistics biostatistics econometrics finance and other changing disciplines now find themselves needing to... Lees meer.