3 papers
stat.ME2022
Modularized Bayesian analyses and cutting feedback in likelihood-free inference
Atlanta Chakraborty, David J. Nott, Christopher Drovandi +2
There has been much recent interest in modifying Bayesian inference for misspecified models so that it is useful for specific purposes. One popular modified Bayesian inference meth…
stat.ME2022
Weakly informative priors and prior-data conflict checking for likelihood-free inference
Atlanta Chakraborty, David J. Nott, Michael Evans
Bayesian likelihood-free inference, which is used to perform Bayesian inference when the likelihood is intractable, enjoys an increasing number of important scientific applications…
stat.AP2020
A robust and non-parametric model for prediction of dengue incidence
Atlanta Chakraborty, Vijay Chandru
Disease surveillance is essential not only for the prior detection of outbreaks but also for monitoring trends of the disease in the long run. In this paper, we aim to build a tact…