Likelihood-free Model Choice for Simulator-based Models with the Jensen--Shannon Divergence

no code implementations • 8 Jun 2022 • Jukka Corander, Ulpu Remes, Timo Koski

Choice of appropriate structure and parametric dimension of a model in the light of data has a rich history in statistical research, where the first seminal approaches were developed in 1970s, such as the Akaike's and Schwarz's model scoring criteria that were inspired by information theory and embodied the rationale called Occam's razor.

Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output

no code implementations • 22 May 2022 • Jukka Corander, Ulpu Remes, Ida Holopainen, Timo Koski

Likelihood-free inference for simulator-based statistical models has recently attracted a surge of interest, both in the machine learning and statistics communities.