Troubled by an inability to find any meaning in his life, the 25-year-old narrator of this deceptively simple novel quits university and eventually arrives at his brother's New York apartment. In a bid to discover what life is all about, he writes lists. He becomes obsessed by time and whether it actually matters. He faxes his meteorologist friend. He endlessly bounces a ball against the wall. He befriends a small boy who lives next door. He yearns to get to the bottom of life and how best to live it. Funny, friendly, enigmatic and frequently poignant - superbly naive.

Written by a prominent analyst Paul. R. Halmos, this book is the most famous, popular, and widely used textbook in the subject. The book is readable for its conciseness and clear explanation. This emended edition is with completely new typesetting and corrections. Asymmetry of the book cover is due to a formal display problem. Actual books are printed symmetrically. Please look at the paperback edition for the correct image. The free PDF file available on the publisher's website www.bowwowpress.org

Contains many examples of contemporary graphic design.

A Naive Realist Theory of Colour defends the view that colours are mind-independent properties of things in the environment. Keith Allen argues that a naive realist theory of colour best explains how colours appear to perceiving subjects, and that this view is not undermined by our modern scientific understanding of the world.

How should one choose the best restaurant to eat in? Can one really make money at gambling? Or predict the future? Naive Decision Making presents the mathematical basis for making decisions where the outcome may be uncertain or the interests of others have to taken into consideration. Professor Körner takes the reader on an enjoyable journey through many aspects of mathematical decision making, with pithy observations, anecdotes and quotations. Topics include probability, statistics, Arrow's theorem, Game Theory and Nash equilibrium. Readers will also gain a great deal of insight into mathematics in general and the role it can play within society. Intended for those with elementary calculus, this book is ideal as a supplementary text for undergraduate courses in probability, game theory and decision making. Engaging and intriguing, it will also appeal to all those of a mathematical mind. To aid understanding, many exercises are included, with solutions available online.

On the day of her sister's marriage introduction, radio show host, Rachel Eden, meets Ejike and Doug; two friends that could not be more different. She finds herself instantly attracted to Ejike, but there's something about Doug and the way he's determined to win her heart. Neither men are who they appear to be, however, making Rachel's choice harder. Her producer and friend, Dongjap, also makes his intentions known, but could he be a little too late?Rachel's Choice is a story of many singles, seeking to know the will of God for their relationships and who need, beyond wisdom, grace to make the right choice for their lives. It is the first volume of a three-book fictional series about love and marriage - The Naive Wife.

This book presents recent advances (from 2008 to 2012) concerning use of the Naïve Bayes model in unsupervised word sense disambiguation (WSD). While WSD, in general, has a number of important applications in various fields of artificial intelligence (information retrieval, text processing, machine translation, message understanding, man-machine communication etc.), unsupervised WSD is considered important because it is language-independent and does not require previously annotated corpora. The Naïve Bayes model has been widely used in supervised WSD, but its use in unsupervised WSD has led to more modest disambiguation results and has been less frequent. It seems that the potential of this statistical model with respect to unsupervised WSD continues to remain insufficiently explored. The present book contends that the Naïve Bayes model needs to be fed knowledge in order to perform well as a clustering technique for unsupervised WSD and examines three entirely different sources of such knowledge for feature selection: WordNet, dependency relations and web N-grams. WSD with an underlying Naïve Bayes model is ultimately positioned on the border between unsupervised and knowledge-based techniques. The benefits of feeding knowledge (of various natures) to a knowledge-lean algorithm for unsupervised WSD that uses the Naïve Bayes model as clustering technique are clearly highlighted. The discussion shows that the Naïve Bayes model still holds promise for the open problem of unsupervised WSD.