BAYESIAN METHODS // Statistical Challenges in Medieval (and Later) Astronomy / Power from Understanding the Shape of Measurement: Progress in Bayesian Inference for Astrophysics / Hierarchical Models, Data Augmentation, and Markov Chain Monte Carlo / Bayesian Adaptive Exploration / Bayesian Model Selection and Analysis for Cepheid Star Oscillations / Bayesian Multiscale Methods for Poisson Count Data // LARGE ASTRONOMICAL DATASETS // NASA's Astrophysics Data Environment / Statistical and Astronomical Challenges in the Sloan Digital Sky Survewy / Challenges for Cluster Analysis in a Virtual Observatory / COSMOLOGY // Statistics of Galaxy Clustering / Analyzing Large Data Sets in Cosmology / The Cosmic Foam: Stochastic Geometry and Spatial Clustering across the Universe / Statistics and the Cosmic Microwave Background / Inference in Microwave Cosmology: A Frequentist Perspective / Nonparametric Inference in Astrophysics // MULTIVARIATE CLUSTERING // Random Forests: Finding Quasars / Interactive and Dynamic Graphics for Data Analysis: A Case Study on Quasar Data / Computational Astrostatistics: Fast and Efficient Tools for Analyzing Huge Astronomical Data Sources / Clustering in High-Dimensional Data Spaces // TIME SERIES AND IMAGE ANALYSIS // Advanced Tools for Astronomical Time Series and Image Analysis / Frequency Estimation and Generalized Lomb-Scargle Periodograms / Multiscale Methods in Astronomy / Threshold Selection in Transform Shrinkage / The Statistical Challenges of Wavelet-Based Source Detection // SUMMARIES // Reflections on SCMA III / An Astronomer's Perspective on SCMA III // CONTRIBUTED PAPERS
Digital sky surveys, high-precision astrometry from satellite data, deep-space data from orbiting telescopes, and the like have all increased the quantity and quality of astronomical data by orders of magnitude per year for several years. Making sense of this wealth of data requires sophisticated statistical techniques. Fortunately, statistical methodologies have similarly made great strides in recent years. Powerful synergies thus emerge when astronomers and statisticians join in examining astrostatistical problems and approaches.
The book begins with an historical overview and tutorial articles on basic cosmology for statisticians and the principles of Bayesian analysis for astronomers. As in earlier volumes in this series, research contributions discussing topics in one field are joined with commentary from scholars in the other. Thus, for example, an overview of Bayesian methods for Poissonian data is joined by discussions of planning astronomical observations with optimal efficiency and nested models to deal with instrumental effects.
The principal theme for the volume is the statistical methods needed to model fundamental characteristics of the early universe on its largest scales.
The quantity and quality of astronomical data has increased by orders of magnitude in recent years; similarly, statistical methodologies have also made great strides recently. This volume shows how powerful synergies emerge when astronomers and statisticians join in examining astrostatistical problems and approaches. Its principal theme is the statistical methods needed to model fundamental characteristics of the early universe on its largest scales.