Imaxe de Portada

Bayesian Compendium

This book describes how Bayesian methods work. Aiming to demystify the approach, it explains how to parameterize and compare models while accounting for uncertainties in data, model parameters and model structures. Bayesian thinking is not difficult and can be used in virtually every kind of researc...

Descrición completa

Gardado en:
Detalles Bibliográficos
Autor Principal: van Oijen, Marcel (Author)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2024.
Edición:2nd ed. 2024.
Subjects:
Statistics .
Biometry.
Ecology .
Environmental monitoring.
Statistical Theory and Methods.
Bayesian Inference.
Biostatistics.
Ecology.
Environmental Monitoring.
Acceso en liña:https://doi.org/10.1007/978-3-031-66085-6
Tags: Engadir etiqueta
Sen Etiquetas, Sexa o primeiro en etiquetar este rexistro!
Table of Contents:
  • - 1. Science and Uncertainty
  • 2. Bayesian Inference
  • 3. Assigning a Prior Distribution
  • 4. Assigning a Likelihood Function
  • 5. Deriving the Posterior Distribution
  • 6. Markov Chain Monte Carlo Sampling (MCMC)
  • 7. Sampling from the Posterior Distribution by MCMC
  • 8. MCMC and Multivariate Models
  • 9. Bayesian Calibration and MCMC: Frequently Asked Questions
  • 10. After the Calibration: Interpretation, Reporting, Visualisation
  • 11. Model Ensembles: BMC and BMA
  • 12. Discrepancy
  • 13. Approximations to Bayes
  • 14.Thirteen Ways to Fit a Straight Line
  • 15. Gaussian Processes and Model Emulation
  • 16. Graphical Modelling
  • 17. Bayesian Hierarchical Modelling
  • 18. Probabilistic Risk Analysis
  • 19. Bayesian Decision Theory
  • 20. Linear Modelling: LM, GLM, GAM and Mixed Models
  • 21. Machine Learning
  • 22. Time Series and Data Assimilation
  • 23. Spatial Modelling and Scaling Error
  • 24. Spatio-Temporal Modelling and Adaptive Sampling
  • 25. What Next?.

Títulos similares