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Bayesian Analysis of Spatially Structured Population Dynamics: Ecological Studies, cartea 253

Autor Qing Zhao
en Limba Engleză Hardback – 31 oct 2024
The book introduces a series of state-of-art Bayesian models that can be used to understand and predict spatially structured population dynamics in our changing world. Several chapters are devoted to introducing models that utilize detection/non-detection data, count data, combined count and capture-recapture data, and spatial capture-recapture data, respectively. The book provides R code of Metropolis-Hastings algorithms that allow efficient computing of these complex models. The book is aimed at graduate students and researchers who are interested in using and further developing these models.
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Specificații

ISBN-13: 9783031645174
ISBN-10: 3031645170
Pagini: 404
Ilustrații: Approx. 130 p. 10 illus.
Dimensiuni: 160 x 241 x 28 mm
Greutate: 0.72 kg
Ediția:2024
Editura: Springer
Colecția Ecological Studies
Seria Ecological Studies

Locul publicării:Cham, Switzerland

Cuprins

Chapter 1: Background.- Chapter 2: Occupancy Models.- Chapter 3: N-mixture models.- Chapter 4: Integrated population models (IPMs).- Chapter 5: Spatial capture-recapture (SCR) models.- Chapter 6: Summary and outlook.

Notă biografică

Zhaowei Chen is Director of the Research Department of Geomechanics for Well Drilling and Completion, Engineering of CNPC Engineering Technology R & D Co., Ltd. He is mainly engaged in the research of geomechanics for unconventional oil and gas wells and deep and ultra-deep wells. He was awarded one second-prize of the CNPC Scientific and Technological Progress Award (ranking first) and one second-prize of the CNPC Basic Research Award (ranking first) of CNPC, and three second-prizes of the Scientific and Technological Progress Award (ranking the first) of CNPC Engineering Technology R & D Co., Ltd.

Xiang Degui, former senior engineer at CNPC Engineering Technology R&D Company (now retired), specialized in directional drilling and casing damage prevention for complex oil and gas wells. During his distinguished career, he led/participated in over 10 national and provincial-level research projects, published 15 technical papers, was granted 9 invention patents, and received 5 research awards.

Qing Zhao is the head of the drilling technology research institute, CNPC Engineering Technology R&D Company Limited. He is mainly engaged in the research and management of new technologies in deep and ultra-deep well drilling & completion engineering. He has led and participated in over 10 national and provincial/ministerial-level research projects. He has published more than 60 papers, authorized more than 30 registered patents, and received more than 10 R&D awards in this field.

Peng Tan is a senior engineer at CNPC Engineering Technology R&D Company Limited. He obtained the "Young Elite Scientists Sponsorship Program" of the China Association for Science and Technology and the "Youth Science and Technology Talent Training Program" of CNPC in 2022 and 2023. He has published more than 30 papers as the first/corresponding author, authorized 13 registered patents, and received 5 R&D awards in this field.


Textul de pe ultima copertă

The book introduces a series of state-of-art Bayesian models that can be used to understand and predict spatially structured population dynamics in our changing world. Several chapters are devoted to introducing models that utilize detection/non-detection data, count data, combined count and capture-recapture data, and spatial capture-recapture data, respectively. The book provides R code of Metropolis-Hasting algorithms that allow efficient computing of these complex models. The book is aimed at graduate students and researchers who are interested in using and further developing these models.

Caracteristici

Synthesizes a vast and diverse set of advanced statistical tools for modeling spatially structured population dynamics Provides sufficiently detailed R code for applying and further developing these models Illustrates the presentation of modeling results with elegant graphs