Bayesian Data Analysis for Animal Scientists Springer International Publishing AG

Książki / Literatura obcojęzyczna

In this book, we provide an easy introduction to Bayesian inference using MCMC techniques, making most topics intuitively reasonable and deriving to appendixes the more complicated matters. The biologist or the agricultural researcher does not normally have a background in Bayesian statistics, having difficulties in following the technical books introducing Bayesian techniques. The difficulties arise from the way of making inferences, which is completely different in the Bayesian school, and from the difficulties in understanding complicated matters such as the MCMC numerical methods. We compare both schools, classic and Bayesian, underlying the advantages of Bayesian solutions, and proposing inferences based in relevant differences, guaranteed values, probabilities of similitude or the use of ratios. We also give a scope of complex problems that can be solved using Bayesian statistics, and we end the book explaining the difficulties associated to model choice and the use of small samples. The book has a practical orientation and uses simple models to introduce the reader in this increasingly popular school of inference.

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Bayesian Data Analysis for Animal Scientists: The Basics

Książki

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Bayesian Survival Analysis

Książki Obcojęzyczne>Angielskie>Mathematics & science>Science: general issues>Maths for scientistsKsiążki Obcojęzyczne>Angielskie>Ma...

0x0020668400000000

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Deep Learning for Physical Scientists - Accelerating Research with Machine Learning John Wiley and Sons Ltd

Książki / Literatura obcojęzyczna

Discover the power of machine learning in the physical sciences with this one-stop resource from a leading voice in the fieldDeep Learning for Physical Scientists: Accelerating Research with Machine Learning delivers an insightful analysis of the transformative techniques being used in deep learning within the physical sciences. The book offers readers the ability to understand, select, and apply the best deep learning techniques for their individual research problem and interpret the outcome.Designed to teach researchers to think in useful new ways about how to achieve results in their research, the book provides scientists with new avenues to attack problems and avoid common pitfalls and problems. Practical case studies and problems are presented, giving readers an opportunity to put what they have learned into practice, with exemplar coding approaches provided to assist the reader.From modelling basics to feed-forward networks, the book offers a broad cross-section of machine learning techniques to improve physical science research. Readers will also enjoy:* A thorough introduction to the basic classification and regression with perceptrons* An exploration of training algorithms, including back propagation and stochastic gradient descent and the parallelization of training* An examination of multi-layer perceptrons for learning from descriptors and de-noising data* Discussions of recurrent neural networks for learning from sequences and convolutional neural networks for learning from images* A treatment of Bayesian optimization for tuning deep learning architecturesPerfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access. Perfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access. This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including: *Basic classification and regression with perceptrons *Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training *Multi-Layer Perceptrons for learning from descriptors, and de-noising data *Recurrent neural networks for learning from sequences *Convolutional neural networks for learning from images *Bayesian optimization for tuning deep learning architectures Each of these areas has direct application to physical science research, and by the end of the book, the reader should feel comfortable enough to select the methodology which is best for their situation, and be able to implement and interpret outcome of the deep learning model. The book is designed to teach researchers to think in new ways, providing them with new avenues to attack problems, and avoid roadblocks within their research. This is achieved through the inclusion of case-study like problems at the end of each chapter, which will give the reader a chance to practice what they have just learnt in a close-to-real-world setting, with example 'solutions' provided through an online resource. Market Description This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including: * Basic classification and regression with perceptrons * Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training * Multi-Layer Perceptrons for learning from descriptors, and de-noising data * Recurrent neural networks for le

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The Design And Statistical Analysis Of Animal Experiments

Książki Obcojęzyczne>Angielskie>Mathematics & science>Science: general issues>Maths for scientistsKsiążki Obcojęzyczne>Angielskie>Ma...

0x014236f600000000

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Handbook of Meta-analysis in Ecology and Evolution Princeton University Press

Książki / Literatura obcojęzyczna

Meta-analysis is a powerful statistical methodology for synthesizing research evidence across independent studies. This is the first comprehensive handbook of meta-analysis written specifically for ecologists and evolutionary biologists, and it provides an invaluable introduction for beginners as well as an up-to-date guide for experienced meta-analysts. The chapters, written by renowned experts, walk readers through every step of meta-analysis, from problem formulation to the presentation of the results. The handbook identifies both the advantages of using meta-analysis for research synthesis and the potential pitfalls and limitations of meta-analysis (including when it should not be used). Different approaches to carrying out a meta-analysis are described, and include moment and least-square, maximum likelihood, and Bayesian approaches, all illustrated using worked examples based on real biological datasets. This one-of-a-kind resource is uniquely tailored to the biological sciences, and will provide an invaluable text for practitioners from graduate students and senior scientists to policymakers in conservation and environmental management.Walks you through every step of carrying out a meta-analysis in ecology and evolutionary biology, from problem formulation to result presentation Brings together experts from a broad range of fields Shows how to avoid, minimize, or resolve pitfalls such as missing data, publication bias, varying data quality, nonindependence of observations, and phylogenetic dependencies among species Helps you choose the right software Draws on numerous examples based on real biological datasets

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Python for Graph and Network Analysis Springer International Publishing AG

Książki / Literatura obcojęzyczna

This research monograph provides the means to learn the theory and practice of graph and network analysis using the Python programming language. The social network analysis techniques, included, will help readers to efficiently analyze social data from Twitter, Facebook, LiveJournal, GitHub and many others at three levels of depth: ego, group, and community. They will be able to analyse militant and revolutionary networks and candidate networks during elections. For instance, they will learn how the Ebola virus spread through communities. Practically, the book is suitable for courses on social network analysis in all disciplines that use social methodology. In the study of social networks, social network analysis makes an interesting interdisciplinary research area, where computer scientists and sociologists bring their competence to a level that will enable them to meet the challenges of this fast-developing field. Computer scientists have the knowledge to parse and process data while sociologists have the experience that is required for efficient data editing and interpretation. Social network analysis has successfully been applied in different fields such as health, cyber security, business, animal social networks, information retrieval, and communications.

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Spalona Żywcem Wyd. Kieszonkowe - Souad

Książki & Multimedia > Książki

Opis - Pierwsze na świecie świadectwo ofiary zbrodni honorowej. Miała siedemnaście lat i zakochała się: zhańbiła rodzinę. Więc rodzina wydała na nią wyrok śmierci... Pokochała go pierwszą miłością. Myślała, że się z nią ożeni. Ale ukochany zniknął, a ona odkryła, że jest w ciąży. A w jej świecie to najcięższa zbrodnia... W zapomnianej przez Boga wiosce w Cisjordanii kobiety są warte mniej niż zwierzęta domowe. Tu mężczyzna jest panem życia i śmierci żony, córki, siostry. Brat może bezkarnie zabić siostrę, matka - córkę, kolejną bezużyteczną dziewczynkę, jaka się urodzi. Tu kobiecie odbiera się godność, a nawet życie zgodnie z odwiecznym obyczajem i uświęconą tradycją. A śmierć jest karą dla dziewczyny, która zhańbi rodzinę. Tak jak Souad. Wyrok wydaje jej ojciec. Szwagier dokonuje egzekucji. Oblewa Souad benzyną i podpala... SOUAD przeżyła - cudem, ale rodzina usiłowała zabić ją nawet w szpitalu. Na zawsze jednak pozostanie straszliwie okaleczona - na ciele i duszy. I wciąż musi się ukrywać; dopóki żyje, jej rodzinę okrywa hańba. Spalona żywcem, opublikowana pod pseudonimem szokująca opowieść o piekle, jakim było jej dzieciństwo i młodość, stała się międzynarodowym bestsellerem. Wydana w 37 w krajach książka przerywa tabu milczenia wobec istniejącej nadal w krajach muzułmańskich barbarzyńskiej tradycji. Nieludzkiego obyczaju, prawa mężczyzn, na mocy którego co najmniej pięć tysięcy kobiet pada co roku ofiarą zbrodni honorowej. Nazwa - Spalona Żywcem Wyd. Kieszonkowe Autor - Souad Oprawa - Miękka Wydawca - Amber Kod ISBN - 9788324159406 Kod EAN - 9788324159406 Wydanie - 1 Rok wydania - 2016 Tłumacz - 31182,maria rostworowska; Format - 110 x 175 x 14 Ilość stron - 224 Podatek VAT - 5% Premiera - 2016-06-23

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Bayes Factors for Forensic Decision Analyses with R Springer International Publishing AG

Książki / Literatura obcojęzyczna

Bayes Factors for Forensic Decision Analyses with R provides a self-contained introduction to computational Bayesian statistics using R. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability-keeping theoretical and philosophical justifications limited. It offers a balanced approach to three naturally interrelated topics:Probabilistic Inference - Relies on the core concept of Bayesian inferential statistics, to help practicing forensic scientists in the logical and balanced evaluation of the weight of evidence.Decision Making - Features how Bayes factors are interpreted in practical applications to help address questions of decision analysis involving the use of forensic science in the law.Operational Relevance - Combines inference and decision, backed up with practical examples and complete sample code in R, including sensitivity analyses and discussion on how to interpret results in context.Over the past decades, probabilistic methods have established a firm position as a reference approach for the management of uncertainty in virtually all areas of science, including forensic science, with Bayes' theorem providing the fundamental logical tenet for assessing how new information-scientific evidence-ought to be weighed. Central to this approach is the Bayes factor, which clarifies the evidential meaning of new information, by providing a measure of the change in the odds in favor of a proposition of interest, when going from the prior to the posterior distribution. Bayes factors should guide the scientist's thinking about the value of scientific evidence and form the basis of logical and balanced reporting practices, thus representing essential foundations for rational decision making under uncertainty.This book would be relevant to students, practitioners, and applied statisticians interested in inference and decision analyses in the critical field of forensic science. It could be used to support practical courses on Bayesian statistics and decision theory at both undergraduate and graduate levels, and will be of equal interest to forensic scientists and practitioners of Bayesian statistics for driving their evaluations and the use of R for their purposes.This book is Open Access.

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BIOLOGY A GLOBAL APPROACH GLOBAL EDITION NOWA URRY WYDAWCA

Podręczniki, artykuły szkolne > Podręczniki do szkół podst. i średnich

Biology: A Global Approach, Global Edition   Biology: A Global Approach   delivers a trusted, accurate, current, and pedagogically innovative experience that guides students to a true understanding of biology. The author team advances Neil Campbell

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Modern Statistics Birkhauser Verlag AG

Książki / Literatura obcojęzyczna

This innovative textbook presents material for a course on modern statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications.  Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail.  A custom Python package is available for download, allowing students to reproduce these examples and explore others.The first chapters of the text focus on analyzing variability, probability models, and distribution functions. Next, the authors introduce statistical inference and bootstrapping, and variability in several dimensions and regression models. The text then goes on to cover sampling for estimation of finite population quantities and time series analysis and prediction, concluding with two chapters on modern data analytic methods. Each chapter includes exercises, data sets, and applications to supplement learning.Modern Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. Because of the foundational nature of the text, it can be combined with any program requiring data analysis in its curriculum, such as courses on data science, industrial statistics, physical and social sciences, and engineering.  Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included. A second, closely related textbook is titled Industrial Statistics: A Computer-Based Approach with Python. It covers topics such as statistical process control, including multivariate methods, the design of experiments, including computer experiments and reliability methods, including Bayesian reliability. These texts can be used independently or for consecutive courses.

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Probability and Statistics for Computer Science Springer International Publishing AG

Książki / Literatura obcojęzyczna

This undergraduate textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Scientists features:- A treatment of random variables and expectations dealing primarily with the discrete case.- A practical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis on Markov chains.- A clear but crisp account of simple point inference strategies (maximum likelihood; Bayesian inference) in simple contexts. This is extended to cover some confidence intervals, samples and populations for random sampling with replacement, and the simplest hypothesis testing.- A chapter dealing with classification, explaining why it's useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods such as random forests and nearest neighbors.- A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems.- A chapter dealing with principal components analysis, developing intuition carefully, and including numerous practical examples. There is a brief description of multivariate scaling via principal coordinate analysis.- A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals.Illustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as boxed Procedures, Definitions, Useful Facts, and Remember This (short tips). Problems and Programming Exercises are at the end of each chapter, with a summary of what the reader should know from that chapter. Instructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides.

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