Renowned for their elaborate and dazzling plumages, the birds of paradise (Paradisaeidae) and bowerbirds (Ptilonohynchidae) exhibit some of the most astonishing behaviours in the avian kingdom. The former is the most iconic group of birds found in New Guinea, while the bowerbirds extend into Australia, and are perhaps best known for the males' construction of avenue bowers, used to tempt females on the forest floor. This comprehensive monograph is dedicated to these two families, combining the product of more than two decades of research and scholarship with original observations by the author and many other knowledgeable contributors. Birds of Paradise and Bowerbirds is the ultimate reference to these two groups. It provides a thorough guide to their identification, taxonomy and ecology, with detailed distribution maps accompanying the text. A series of beautifully illustrated plates by Richard Allen cover all of the 108 recognised taxa in these groups, with these supplemented by more than 200 photographs covering a range of racial and age-related plumage variety. This book is an indispensable addition to the libraries of all birders and ornithologists interested in these sensational birds.

Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica (R) notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.

Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica (R) notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.