Steffen Lauritzen – författare
Visar alla böcker från författaren Steffen Lauritzen. Handla med fri frakt och snabb leverans.
4 produkter
4 produkter
908 kr
Skickas inom 10-15 vardagar
A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models facilitate tractable computation with multivariate distributions, making the models a valuable tool with a plethora of applications. Furthermore, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference.While there exist a number of excellent books on graphical models, the field has grown so much that individual authors can hardly cover its entire scope. Moreover, the field is interdisciplinary by nature. Through chapters by leading researchers from different areas, this handbook provides a broad and accessible overview of the state of the art.Features: Contributions by leading researchers from a range of disciplinesStructured in five parts, covering foundations, computational aspects, statistical inference, causal inference, and applicationsBalanced coverage of concepts, theory, methods, examples, and applicationsChapters can be read mostly independently, while cross-references highlight connectionsThe handbook is targeted at a wide audience, including graduate students, applied researchers, and experts in graphical models.
1 195 kr
Skickas inom 10-15 vardagar
Fundamentals of Mathematical Statistics is meant for a standard one-semester advanced undergraduate or graduate-level course in Mathematical Statistics. It covers all the key topics—statistical models, linear normal models, exponential families, estimation, asymptotics of maximum likelihood, significance testing, and models for tables of counts. It assumes a good background in mathematical analysis, linear algebra, and probability but includes an appendix with basic results from these areas. Throughout the text, there are numerous examples and graduated exercises that illustrate the topics covered, rendering the book suitable for teaching or self-study. FeaturesA concise yet rigorous introduction to a one-semester course in Mathematical StatisticsCovers all the key topicsAssumes a solid background in Mathematics and ProbabilityNumerous examples illustrate the topicsMany exercises enhance understanding of the material and enable course useThis textbook will be a perfect fit for an advanced course in Mathematical Statistics or Statistical Theory. The concise and lucid approach means it could also serve as a good alternative, or supplement, to existing texts.
852 kr
Skickas inom 10-15 vardagar
Graphical models in their modern form have been around since the late 1970s and appear today in many areas of the sciences. Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years. In recent years many of these software developments have taken place within the R community, either in the form of new packages or by providing an R interface to existing software. This book attempts to give the reader a gentle introduction to graphical modeling using R and the main features of some of these packages. In addition, the book provides examples of how more advanced aspects of graphical modeling can be represented and handled within R. Topics covered in the seven chapters include graphical models for contingency tables, Gaussian and mixed graphical models, Bayesian networks and modeling high dimensional data.
1 919 kr
Skickas inom 10-15 vardagar
A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models facilitate tractable computation with multivariate distributions, making the models a valuable tool with a plethora of applications. Furthermore, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference.While there exist a number of excellent books on graphical models, the field has grown so much that individual authors can hardly cover its entire scope. Moreover, the field is interdisciplinary by nature. Through chapters by leading researchers from different areas, this handbook provides a broad and accessible overview of the state of the art.Key features:* Contributions by leading researchers from a range of disciplines* Structured in five parts, covering foundations, computational aspects, statistical inference, causal inference, and applications* Balanced coverage of concepts, theory, methods, examples, and applications* Chapters can be read mostly independently, while cross-references highlight connectionsThe handbook is targeted at a wide audience, including graduate students, applied researchers, and experts in graphical models.