Bayesian Mixer

Principled Bayesian Workflow

On Thursday evening Michael Betancourt gave an insightful and thought provoking talk on Principled Bayesian Workflow at the Baysian Mixer Meetup, hosted by QuantumBlack. Michael is an applied statistician, conslutant, co-developer of Stan and passionate educator of Bayesian modelling. What is a principled Bayesian workflow? It turns out that it mimics my idea of the scientific method: Create a model for the ‘small world’ of interest, i.e. the small world relevant to test an idea, e.

Changing settlement rate model for paid losses

Last week I wrote about Glenn Meyers’ correlated log-normal chain-ladder model (CCL), which he presented at the 10th Bayesian Mixer Meetup. Today, I will continue with a variant Glenn also discussed: The changing settlement log-normal chain-ladder model (CSR). Glenn used the correlated log-normal chain-ladder model on reported incurred claims data to predict future developments. However, when looking at paid claims data, Glenn suggested to change the model slightly. Instead allowing for correlation across accident years, he allows for a gradual shift in the payout pattern to account for a change in the claim settlement rate across accident years.

Notes from 4th Bayesian Mixer Meetup

Last Tuesday we got together for the 4th Bayesian Mixer Meetup. Product Madness kindly hosted us at their offices in Euston Square. About 50 Bayesians came along; the biggest turn up thus far, including developers of PyMC3 (Peadar Coyle) and Stan (Michael Betancourt). The agenda had two feature talks by Dominic Steinitz and Volodymyr Kazantsev and a lightning talk by Jon Sedar. Dominic Steinitz: Hamiltonian and Sequential MC samplers to model ecosystems Dominic shared with us his experience of using Hamiltonian and Sequential Monte Carlo samplers to model ecosystems.