Author: Vedant Chandra
Title: Stochastic Compartmental Modelling of SARS-CoV-2 with Approximate Bayesian Computation Document date: 2020_4_1
ID: itviia7v_9
Snippet: As the parameters converge to their posterior, the shrinkage of slows down, and the sampler has to reject progressively more particles in order to accumulate p particles with d < . We choose a stopping criterion such that the acceptance ratio (number of total particles sampled in order to accumulate p valid particles) is 1%. We find that the models are well-converged at this point, and sampling further does not improve the parameter posteriors. T.....
Document: As the parameters converge to their posterior, the shrinkage of slows down, and the sampler has to reject progressively more particles in order to accumulate p particles with d < . We choose a stopping criterion such that the acceptance ratio (number of total particles sampled in order to accumulate p valid particles) is 1%. We find that the models are well-converged at this point, and sampling further does not improve the parameter posteriors. The root mean square difference defined in Eqn. 2 is around d ∼ 10 for the converged models. Each fit takes ∼ 25 minutes to complete on a regular laptop computer. Figure 3 . Extrapolated infection curves for the 10 worstaffected Chinese provinces. We allow the epidemic solution to continue until no active infections remain.
Search related documents:
Co phrase search for related documents, hyperlinks ordered by date