Author: Lloyd A. C. Chapman; Simon E. F. Spencer; Timothy M. Pollington; Chris P. Jewell; Dinesh Mondal; Jorge Alvar; T. Deirdre Hollingsworth; Mary M. Cameron; Caryn Bern; Graham F. Medley
Title: Inferring transmission trees to guide targeting of interventions against visceral leishmaniasis and post-kala-azar dermal leishmaniasis Document date: 2020_2_25
ID: nqn1qzcu_65
Snippet: However, we are in a context with a large amount of missing data, which is strongly correlated with some of the transmission 456 parameters (see Parameter estimates below), so the posterior distribution is not symmetric, and this scaling is not optimal. 457 We therefore follow (36) and scale c k adaptively as the algorithm progresses to target an acceptance rate of approximately 458 23.4% for updates to -. We do this by rescaling c k by a factor .....
Document: However, we are in a context with a large amount of missing data, which is strongly correlated with some of the transmission 456 parameters (see Parameter estimates below), so the posterior distribution is not symmetric, and this scaling is not optimal. 457 We therefore follow (36) and scale c k adaptively as the algorithm progresses to target an acceptance rate of approximately 458 23.4% for updates to -. We do this by rescaling c k by a factor of x k > 1 every time an acceptance occurs and by a factor of 459 x ‹/(‹≠1) k < 1 every time a rejection occurs such that the acceptance rate ‹ approaches 23.4% in the long run,
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