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R coding involving writing functions

Approximate Bayesian computation for disease outbreaks: Epidemiologists have developed models

for disease spread, but these models often lead to intractable likelihoods. One strategy

for fitting parameters in such models is approximate Bayesian computation (ABC), which

is a simulation-based method for fitting models to data (we will discuss ABC in detail later

in the course).

You will write functions that perform ABC to fit parameters in a model of the spread of a

virus. This will involve:

– Drawing parameters from a prior distribution,

1

– Drawing data according to a probability model given the parameters,

– Computing a similarity measure between the simulated data and an observed dataset,

and finally

– Keeping or discarding the samples based on that similarity.

The goal will be to recreate Figures 3a and 3c in Tony and Stumpf, “Simulation-based model

selection for dynamical systems in systems and population biology”, Bioinformatics (2010)

(available at [login to view URL]), with

the data provided in the supplement to that paper.

Kĩ năng: Khoa học dữ liệu, Ngôn ngữ lập trình R, Phân tích thống kê, Statistics

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