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The subject of the book is the grounding of architectural acoustics in psychophysics (perception, listener preferences) and neuroscience (auditory function, neural correlates of perception and preference). This is the first rational-scientific approach to designing performance spaces that is based on systematic psychoacoustical observations of spatial hearing and listener preferences. Observations of the neuronal correlates of auditory qualities and listener preferences ground the theory of listener preferences in the neurophysiology of the human brain. A correlation-based model of...

In an expansion of his 2012 Robson Classical Lectures, Clifford Ando examines the connection between the nature of the Latin language and Roman thinking about law, society, and empire. Drawing on innovative work in cognitive linguistics and anthropology, Roman Social Imaginaries considers how metaphor, metonymy, analogy, and ideation helped create the structures of thought that shaped the Roman Empire as a political construct.

Beginning in early Roman history, Ando shows how the expansion of the empire into new territories led the Romans to develop and exploit Latin's...

The Fukushima Daiichi nuclear accident that followed the March 2011 tsunami and earthquake in Japan shocked the world. In the wake the of the disaster, questions were asked as to why Japanese antinuclear movements were not able to prevent those with vested interests, such as businesses, bureaucrats, the media and academics, from facilitating nuclear energy policies? Taking this question as its starting point, this book looks more widely at the development and powerlessness of Japanese civil society, and seeks to untangle this intersection between social movements and civil society in...

Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model selection procedures. It thoroughly explains the concepts, illustrates the derivations of various Bayesian model selection criteria through examples, and provides R code for implementation.

The author shows how to implement a variety of Bayesian inference using R and sampling methods, such as Markov chain Monte Carlo. He covers the different types of simulation-based Bayesian model selection criteria, including the...