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In this chapter we consider how approximate Bayesian computation (ABC) methods can be used in estimating the (unknown) parameters θ of a parametric model (stochastic process) ℳ , where in our case the parametric model ℳ will be a stochastic epidemic process. We assume that ℳ gives rise to realizations X with the observed data x ∗ being one such realization. The foundations for ABC, along with all Bayesian statistics, is Bayes’ Theorem which states that 10.1 π ( θ | x ∗ ) = π ( x ∗ | θ ) π ( θ ) π ( x ∗ ) ,
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