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In this richly illustrated book, a range of accessible examples are used to show how Bayes' rule is actually a natural consequence of commonsense reasoning. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for the novice who wishes to become familiar with the basic principles of Bayesian analysis.
Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, a range of accessible examples is used to show how Bayes' rule is actually a natural consequence of common sense reasoning. Bayes' rule is then derived using intuitive graphical representations of probability, and Bayesian analysis is applied to parameter estimation using the MatLab and Python programs provided online. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to become familiar with the basic principles of Bayesian analysis. Note that this MatLab version of Bayes' Rule includes working MatLab code snippets alongside the relevant equations.
Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, a range of accessible examples is used to show how Bayes' rule is actually a natural consequence of common sense reasoning. Bayes' rule is then derived using intuitive graphical representations of probability, and Bayesian analysis is applied to parameter estimation. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to become familiar with the basic principles of Bayesian analysis. Note that this book includes Python (3.0) code snippets, which reproduce key numerical results and diagrams.
Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, a range of accessible examples is used to show how Bayes' rule is actually a natural consequence of common sense reasoning. Bayes' rule is then derived using intuitive graphical representations of probability, and Bayesian analysis is applied to parameter estimation. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to become familiar with the basic principles of Bayesian analysis. Note that this book includes R (3.2) code snippets, which reproduce key numerical results and diagrams.
This book presents the most important ideas behind Bayes’ Rule in a form suitable for the general reader. It is written without formulae because they are not necessary; the ability to add and multiply is all that is needed. As well as showing in full the application of Bayes’ Rule to some quantitatively simple, though not trivial, examples, the book also convincingly demonstrates that some familiarity with Bayes’ Rule is helpful in thinking about how best to structure one’s thinking.
What is the origin of our universe? What are dark matter and dark energy? What is our role in the universe as human beings capable of knowledge? What makes us intelligent cognitive agents seemingly endowed with consciousness? Scientific research across both the physical and cognitive sciences raises fascinating philosophical questions. Philosophy and the Sciences For Everyone introduces these questions and more. It begins by asking what good is philosophy for the sciences before examining the following questions: The origin of our universe Dark matter and dark energy Anthropic reasoning in philosophy and cosmology Evolutionary theory and the human mind What is consciousness? Intelligent machines and the human brain Embodied Cognition. Each chapter includes an introduction, summary and study questions and there is a glossary of technical terms. Designed to be used on the corresponding Philosophy and the Sciences online course offered by the University of Edinburgh this book is also a superb introduction to central topics in philosophy of science and popular science.
This volume answers the question: Why do we believe what we believe? It examines current research on the concept of beliefs, and the development in our understanding of the process of believing. It takes into account empirical findings in the field of neuroscience regarding the processes that underlie beliefs, and discusses the notion that beyond the interactive exploratory analysis of sensory information from the complex outside world, humans engage in an evaluative analysis by which they attribute personal meaning and relevance to the probabilistic representations of objects and events. Beliefs exert a strong influence on behaviour, decision-making, and identifying and solving problems. Despite their importance, beliefs have until recently not been at the centre of scientific interest. In fact, “belief” is an ill-defined phenomenon. From a transdisciplinary perspective the actual approaches to understanding belief seem incompatible as they attempt to highlight such different topics as “belief – religion”, “belief – spirituality”, “belief – faith”, “belief – knowledge”, “belief – attitude”, “belief – disbelief”, “belief – illusion”, and “believing – brain function”. This situation contradicts the idea that belief is close to pathological phenomena and that it should be eliminated from scientific discussions. Rather, believing is fundamental for understanding the many problems of every-day life. In fact, the book shows that beliefs are relevant for politics, international affairs, economy, law, or religions also in modern societies. This book presents the increasing scientific interest in beliefs and believing, and reflects the change in focus from the content aspect of belief towards the fluid nature of believing.

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