Distribution Models Theory PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Distribution Models Theory PDF full book. Access full book title Distribution Models Theory by Rafael Herrerias-pleguezuelo. Download full books in PDF and EPUB format.
Author: Rafael Herrerias-pleguezuelo
Publisher: World Scientific
ISBN: 9814477400
Category : Business & Economics
Languages : en
Pages : 307
Get Book Here
Book Description
Distribution Models Theory is a revised edition of papers specially selected by the Scientific Committee for the Fifth Workshop of Spanish Scientific Association of Applied Economy on Distribution Models Theory held in Granada (Spain) in September 2005. The contributions offer a must-have point of reference on models theory.This book has been selected for coverage in:• Index to Scientific & Technical Proceedings® (ISTP®/ISI Proceedings)• Index to Scientific & Technical Proceedings (ISTP CDROM version/ISI Proceedings)
Author: Rafael Herrerias-pleguezuelo
Publisher: World Scientific
ISBN: 9814477400
Category : Business & Economics
Languages : en
Pages : 307
Get Book Here
Book Description
Distribution Models Theory is a revised edition of papers specially selected by the Scientific Committee for the Fifth Workshop of Spanish Scientific Association of Applied Economy on Distribution Models Theory held in Granada (Spain) in September 2005. The contributions offer a must-have point of reference on models theory.This book has been selected for coverage in:• Index to Scientific & Technical Proceedings® (ISTP®/ISI Proceedings)• Index to Scientific & Technical Proceedings (ISTP CDROM version/ISI Proceedings)
Author: Antoine Guisan
Publisher: Cambridge University Press
ISBN: 0521765137
Category : Computers
Languages : en
Pages : 513
Get Book Here
Book Description
This book introduces the key stages of niche-based habitat suitability model building, evaluation and prediction required for understanding and predicting future patterns of species and biodiversity. Beginning with the main theory behind ecological niches and species distributions, the book proceeds through all major steps of model building, from conceptualization and model training to model evaluation and spatio-temporal predictions. Extensive examples using R support graduate students and researchers in quantifying ecological niches and predicting species distributions with their own data, and help to address key environmental and conservation problems. Reflecting this highly active field of research, the book incorporates the latest developments from informatics and statistics, as well as using data from remote sources such as satellite imagery. A website at www.unil.ch/hsdm contains the codes and supporting material required to run the examples and teach courses.
Author: Otso Ovaskainen
Publisher: Cambridge University Press
ISBN: 1108492460
Category : Nature
Languages : en
Pages : 389
Get Book Here
Book Description
A comprehensive account of joint species distribution modelling, covering statistical analyses in light of modern community ecology theory.
Author: William Christian Krumbein
Publisher:
ISBN:
Category : Beach erosion
Languages : en
Pages : 30
Get Book Here
Book Description
Author: Antoine Guisan
Publisher: Cambridge University Press
ISBN: 1108508499
Category : Nature
Languages : en
Pages : 513
Get Book Here
Book Description
This book introduces the key stages of niche-based habitat suitability model building, evaluation and prediction required for understanding and predicting future patterns of species and biodiversity. Beginning with the main theory behind ecological niches and species distributions, the book proceeds through all major steps of model building, from conceptualization and model training to model evaluation and spatio-temporal predictions. Extensive examples using R support graduate students and researchers in quantifying ecological niches and predicting species distributions with their own data, and help to address key environmental and conservation problems. Reflecting this highly active field of research, the book incorporates the latest developments from informatics and statistics, as well as using data from remote sources such as satellite imagery. A website at www.unil.ch/hsdm contains the codes and supporting material required to run the examples and teach courses.
Author: Peter Buchholz
Publisher: Springer
ISBN: 3319066749
Category : Mathematics
Languages : en
Pages : 137
Get Book Here
Book Description
Containing a summary of several recent results on Markov-based input modeling in a coherent notation, this book introduces and compares algorithms for parameter fitting and gives an overview of available software tools in the area. Due to progress made in recent years with respect to new algorithms to generate PH distributions and Markovian arrival processes from measured data, the models outlined are useful alternatives to other distributions or stochastic processes used for input modeling. Graduate students and researchers in applied probability, operations research and computer science along with practitioners using simulation or analytical models for performance analysis and capacity planning will find the unified notation and up-to-date results presented useful. Input modeling is the key step in model based system analysis to adequately describe the load of a system using stochastic models. The goal of input modeling is to find a stochastic model to describe a sequence of measurements from a real system to model for example the inter-arrival times of packets in a computer network or failure times of components in a manufacturing plant. Typical application areas are performance and dependability analysis of computer systems, communication networks, logistics or manufacturing systems but also the analysis of biological or chemical reaction networks and similar problems. Often the measured values have a high variability and are correlated. It’s been known for a long time that Markov based models like phase type distributions or Markovian arrival processes are very general and allow one to capture even complex behaviors. However, the parameterization of these models results often in a complex and non-linear optimization problem. Only recently, several new results about the modeling capabilities of Markov based models and algorithms to fit the parameters of those models have been published.
Author: Lin Zhang
Publisher: Springer
ISBN: 9811026696
Category : Computers
Languages : en
Pages : 660
Get Book Here
Book Description
This four-volume set (CCIS 643, 644, 645, 646) constitutes the refereed proceedings of the 16th Asia Simulation Conference and the First Autumn Simulation Multi-Conference, AsiaSim / SCS AutumnSim 2016, held in Beijing, China, in October 2016. The 265 revised full papers presented were carefully reviewed and selected from 651 submissions. The papers in this third volume of the set are organized in topical sections on Cloud technologies in simulation applications; fractional calculus with applications and simulations; modeling and simulation for energy, environment and climate; SBA virtual prototyping engineering technology; simulation and Big Data.
Author: Bent Jorgensen
Publisher: CRC Press
ISBN: 9780412997112
Category : Mathematics
Languages : en
Pages : 264
Get Book Here
Book Description
The theory of dispersion models straddles both statistics and probability, and involves an encyclopedic collection of tools, such as exponential families, asymptotic theory, stochastic processes, Tauber theory, infinite divisibility, and stable distributions. The Theory of Dispersion Models introduces the reader to these models, which serve as error distributions for generalized linear models, and looks at their applications within this context.
Author: Alexander Zeifman
Publisher: MDPI
ISBN: 3036504524
Category : Mathematics
Languages : en
Pages : 370
Get Book Here
Book Description
The aim of this Special Issue of Mathematics is to commemorate the outstanding Russian mathematician Vladimir Zolotarev, whose 90th birthday will be celebrated on February 27th, 2021. The present Special Issue contains a collection of new papers by participants in sessions of the International Seminar on Stability Problems for Stochastic Models founded by Zolotarev. Along with research in probability distributions theory, limit theorems of probability theory, stochastic processes, mathematical statistics, and queuing theory, this collection contains papers dealing with applications of stochastic models in modeling of pension schemes, modeling of extreme precipitation, construction of statistical indicators of scientific publication importance, and other fields.
Author: Paul-Andre Monney
Publisher: Springer Science & Business Media
ISBN: 3642517463
Category : Business & Economics
Languages : en
Pages : 160
Get Book Here
Book Description
The subject of this book is the reasoning under uncertainty based on sta tistical evidence, where the word reasoning is taken to mean searching for arguments in favor or against particular hypotheses of interest. The kind of reasoning we are using is composed of two aspects. The first one is inspired from classical reasoning in formal logic, where deductions are made from a knowledge base of observed facts and formulas representing the domain spe cific knowledge. In this book, the facts are the statistical observations and the general knowledge is represented by an instance of a special kind of sta tistical models called functional models. The second aspect deals with the uncertainty under which the formal reasoning takes place. For this aspect, the theory of hints [27] is the appropriate tool. Basically, we assume that some uncertain perturbation takes a specific value and then logically eval uate the consequences of this assumption. The original uncertainty about the perturbation is then transferred to the consequences of the assumption. This kind of reasoning is called assumption-based reasoning. Before going into more details about the content of this book, it might be interesting to look briefly at the roots and origins of assumption-based reasoning in the statistical context. In 1930, R. A. Fisher [17] defined the notion of fiducial distribution as the result of a new form of argument, as opposed to the result of the older Bayesian argument.
