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Applied Stochastic Differential Equations
With this hands-on introduction readers will learn what SDEs are all about and how they should use them in practice.
Image Analysis
This volume constitutes the refereed proceedings of the 21st Scandinavian Conference on Image Analysis, SCIA 2019, held in Norrköping, Sweden, in June 2019. The 40 revised papers presented were carefully reviewed and selected from 63 submissions. The contributions are structured in topical sections on Deep convolutional neural networks; Feature extraction and image analysis; Matching, tracking and geometry; and Medical and biomedical image analysis.
Image Analysis
This book constitutes the refereed proceedings of the 18th Scandinavian Conference on Image Analysis, SCIA 2013, held in Espoo, Finland, in June 2013. The 67 revised full papers presented were carefully reviewed and selected from 132 submissions. The papers are organized in topical sections on feature extraction and segmentation, pattern recognition and machine learning, medical and biomedical image analysis, faces and gestures, object and scene recognition, matching, registration, and alignment, 3D vision, color and multispectral image analysis, motion analysis, systems and applications, human-centered computing, and video and multimedia analysis.
Exponential Families in Theory and Practice
During the past half-century, exponential families have attained a position at the center of parametric statistical inference. Theoretical advances have been matched, and more than matched, in the world of applications, where logistic regression by itself has become the go-to methodology in medical statistics, computer-based prediction algorithms, and the social sciences. This book is based on a one-semester graduate course for first year Ph.D. and advanced master's students. After presenting the basic structure of univariate and multivariate exponential families, their application to generalized linear models including logistic and Poisson regression is described in detail, emphasizing geometrical ideas, computational practice, and the analogy with ordinary linear regression. Connections are made with a variety of current statistical methodologies: missing data, survival analysis and proportional hazards, false discovery rates, bootstrapping, and empirical Bayes analysis. The book connects exponential family theory with its applications in a way that doesn't require advanced mathematical preparation.
Statistical Modelling by Exponential Families
A readable, digestible introduction to essential theory and wealth of applications, with a vast set of examples and numerous exercises.
Principles of Statistical Analysis
This concise course in data analysis and inference for the mathematically literate builds on survey sampling and designed experiments.
Scheduling and Control of Queueing Networks
A graduate text on theory and methods using applied probability techniques for scheduling service, manufacturing, and information networks.
Computational Bayesian Statistics
This integrated introduction to fundamentals, computation, and software is your key to understanding and using advanced Bayesian methods.
Bayesian Filtering and Smoothing
A Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
Machine Learning
Presents carefully selected supervised and unsupervised learning methods from basic to state-of-the-art,in a coherent statistical framework.
