A ground-breaking innovation in epistemology Moss shows how the idea of probabilistic knowledge and belief illuminates issues in many areas of philosophy Traditional philosophical discussions of knowledge have focused on the epistemic status of full beliefs. Sarah Moss argues that in addition to full beliefs, credences can constitute knowledge. For instance, your 0.4 credence t...(展开全部)

For the past forty years, linguistics has been dominated by the idea that language is categorical and linguistic competence discrete. It has become increasingly clear, however, that many levels of representation, from phonemes to sentence structure, show probabilistic properties, as does the language faculty. Probabilistic linguistics conceptualizes categories as distributions ...(展开全部)

Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are ba...(展开全部)

Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are ba...(展开全部)

The Probabilistic Method, Fourth Edition is an ideal textbook for upper-undergraduate and graduate-level students majoring in mathematic s, computer science, operations research, and statistics. The Fourth Edition is also an excellent reference for researchers and combinatorists who use probabilistic methods, discrete mathematics, and number theory.

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (inc...(展开全部)

Praise for the Second Edition : "Serious researchers in combinatorics or algorithm design will wish to read the book in its entirety...the book may also be enjoyed on a lighter level since the different chapters are largely independent and so it is possible to pick out gems in one's own area..." — Formal Aspects of Computing This Third Edition of The Probabilistic Method ...(展开全部)

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, a unified, probabilistic a...(展开全部)

Kevin P. Murphy Kevin Patrick Murphy was born in Ireland, grew up in England (BA from Cambridge), and went to graduate school in the USA (MEng from U. Penn, PhD from UC Berkeley, Postdoc at MIT). In 2004, he became a professor of computer science and statistics at the University of British Columbia in Vancouver, Canada. In 2011, he went to Google in Mountain View, California fo...(展开全部)

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, a unified, probabilistic a...(展开全部)