Bayesian Analysis of Infectious Diseases
- Indbinding:
- Paperback
- Sideantal:
- 332
- Udgivet:
- 29. august 2022
- Størrelse:
- 234x22x155 mm.
- Vægt:
- 524 g.
- 2-3 uger.
- 11. december 2024
På lager
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- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Bayesian Analysis of Infectious Diseases
Bayesian Analysis of Infectious Diseases -COVID-19 and Beyond shows how the Bayesian approach can be used to analyze the evolutionary behavior of infectious diseases, including the coronavirus pandemic. The book describes the foundation of Bayesian statistics while explicating the biology and evolutionary behavior of infectious diseases, including viral and bacterial manifestations of the contagion. The book discusses the application of Markov Chains to contagious diseases, previews data analysis models, the epidemic threshold theorem, and basic properties of the infection process. Also described are the chain binomial model for the evolution of epidemics.
Features:
Represents the first book on infectious disease from a Bayesian perspective.
Employs WinBUGS and R to generate observations that follow the course of contagious maladies.
Includes discussion of the coronavirus pandemic as well as many examples from the past, including the flu epidemic of 1918-1919.
Compares standard non-Bayesian and Bayesian inferences.
Offers the R and WinBUGS code on at www.routledge.com/9780367633868
Features:
Represents the first book on infectious disease from a Bayesian perspective.
Employs WinBUGS and R to generate observations that follow the course of contagious maladies.
Includes discussion of the coronavirus pandemic as well as many examples from the past, including the flu epidemic of 1918-1919.
Compares standard non-Bayesian and Bayesian inferences.
Offers the R and WinBUGS code on at www.routledge.com/9780367633868
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