Fiona O'Brien
Fiona Roberton
Fiona Roberton
Fiona Collins
Fiona Davis
Fiona Gibson
Fiona O'Brien
Fiona Probyn-Rapsey
Fiona McHardy
At the heart of this study is the violent crime committed in ancient greek society against women and by women.
Fiona Barton
Fiona Thomas
Fiona Probyn-Rapsey
Fiona McHardy
At the heart of this study is the violent crime committed in ancient greek society against women and by women.
Fiona McArthur
Fiona Undrill
Fiona Tomlinson
Fiona Bloomer
Fiona Robertson
Fiona Tolan
Fiona Snyckers
Fiona Buckley
Fiona Fui-Hoon Nah
Fiona Sarola
Fiona Benson
Fiona Vigo Marshall
Fiona Robinson
Fiona Ong
Fiona Andreallo
Fiona E. Matthews
Fiona Ostby
Fiona Cummins
Fiona Weir Walmsley
Fiona Macmillan
Fiona Morris
Fiona McArthur
Fiona Watt
Fiona Marsden
Fiona Parker
Fiona Erskine
Fiona Hum
Fiona Palmer
Fiona Macdonald
Fiona Watt
Fíona Scarlett
Fiona Parashar
Fiona Candlin
Fiona Davis
Lillian was one of the most sought after artists' models in new york city.
Fiona Murphy
Fiona Lowenstein
Fiona M. Lewis
Fiona O'Brien
Fiona Woodcock
Fiona Parashar
Fiona Benson
Gennady Martynov
This book is devoted to the theory of goodness-of-fit tests based on weighted empirical processes.
Gennady Martynov
This book is devoted to the theory of goodness-of-fit tests based on weighted empirical processes.
Karl Stromberg
Farrow
Valen E. Johnson
N. Balakrishnan
Henry Freiser
Mahendra Mallick
Rex F. Galbraith
Alex Cobham
In the global race to reach the end of aids, why is the world slipping off track?
Svein Linge
Alan Bryman
The 20th anniversary edition of this groundbreaking and bestselling volume offers powerful examples of the mathematics that can develop the thinking of elementary school children.
Hannelore Lisei
Andrew Metcalfe
W. Holmes Finch
Like its bestselling predecessor, multilevel modeling using r, second edition provides the reader with a helpful guide to conducting multilevel data modeling using the r software environment.
Christer Thrane
This book is an introduction to regression analysis focusing on the practicalities of doing regression analysis on real life data.
Miriam Reußner
Strukturgleichungsmodelle bieten eine flexible modellierungsvariante an, die sich auch in den sozialwissenschaften immer gr��erer beliebtheit erfreut.
Mustapha Akinkunmi
Business statistics with solutions in r covers a wide range of applications of statistics in solving business related problems.
Lyle D. Broemeling
In many branches of science relevant observations are taken sequentially over time.
Peter J. Diggle
John MacInnes
Brian J. Reich
Bayesian statistical methods provides data scientists with the foundational and computational tools needed to carry out a bayesian analysis.
Joshua Chan
Bayesian econometric methods examines principles of bayesian inference by posing a series of theoretical and applied questions and providing detailed solutions to those questions.
Morgan, George A.
Designed to help students analyze and interpret research data using ibm spss, this book describes the use of statistics in user-friendly, non-technical language to show readers how to choose the appropriate statistic based on the design, interpret outp.
M. Antónia Amaral Turkman
Meaningful use of advanced bayesian methods requires a good understanding of the fundamentals.
Peter J. Diggle
William Navidi
Physics 1st edition is a spin-off of the market leading college physics 2nd edition text by giambattista/richardson/richardson.
George David Garson
Multilevel modeling is a concise, practical guide to building models for multilevel and longitudinal data.
Charef Harrats
Felix Bittmann
Morgan, George A.
Designed to help students analyze and interpret research data using ibm spss, this book describes the use of statistics in user-friendly, non-technical language to show readers how to choose the appropriate statistic based on the design, interpret outp.
Richard P. Feynman
Statistical mechanics is a comprehensive text intended for physics students.
David J. Balding
A timely update of a highly popular handbook on statistical genomics this new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field.
Brian J. Reich
Bayesian statistical methods provides data scientists with the foundational and computational tools needed to carry out a bayesian analysis.
Lyle D. Broemeling
In many branches of science relevant observations are taken sequentially over time.
Peter D. Congdon
Revised edition of: applied bayesian hierarchical methods.
David R. Boniface
Experiment design and statistical methods introduces the concepts, principles, and techniques for carrying out a practical research project either in real world settings or laboratories - relevant to studies in psychology, education, life sciences, social.
G Geoffrey Vining
Mevin B. Hooten
Bringing bayesian models to life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental data analysis.
Yury Gogotsi
In many instances of mechanical interaction between two materials, the physicalcontact affects only the outermost surface layer, with little discernible influence on the bulk of the material.
Yan Liu
Demonstrates how to solve reliability problems using practical applications of bayesian modelsthis self-contained reference provides fundamental knowledge of bayesian reliability and utilizes numerous examples to show how bayesian model.
Norman Fenton
Since the first edition of this book published, bayesian networks have become even more important for applications in a vast array of fields.
Richard McElreath
Statistical rethinking: a bayesian course with examples in r and stan builds readers' knowledge of and confidence in statistical modeling.
Pierre Moulin
This book is a mathematically accessible and up-to-date introduction to the tools needed to address modern inference problems in engineering and data science, ideal for graduate students taking courses on statistical inference and detection and estimation.
Rosario O. Cardenas
Sumio Watanabe
Mathematical theory of bayesian statistics introduces the mathematical foundation of bayesian inference which is well-known to be more accurate in many real-world problems than the maximum likelihood method.
Marciel Agop
The authors examine topics in modern physics and offer a unitary and original treatment of the fundamental problems of the dynamics of physical systems, as well as a description of the nuclear matter within a framework of general relativity.
J. S. Maritz
The second edition of empirical bayes methods details are provided of the derivation and the performance of empirical bayes rules for a variety of special models.
Jim Q. Smith
Written by some major contributors to the development of this class of graphical models, chain event graphs introduces a viable and straightforward new tool for statistical inference, model selection and learning techniques.
Bryant, John
Bayesian demographic estimation and forecasting presents three statistical frameworks for modern demographic estimation and forecasting.
Setsuo Ichimaru
Plasma physics is an integral part of statistical physics, complete with its own basic theories.