8 citations · 11 across the 5 of their papers we have counts for
5 papers
Species Sensitivity Distribution revisited: a Bayesian nonparametric approach
Louise Alamichel, Julyan Arbel, Guillaume Kon Kam King +1
We present a novel approach to ecological risk assessment by recasting the Species Sensitivity Distribution (SSD) method within a Bayesian nonparametric (BNP) framework. Widely man…
Approximate filtering via discrete dual processes
Guillaume Kon Kam King, Andrea Pandolfi, Marco Piretto +1
We consider the task of filtering a dynamic parameter evolving as a diffusion process, given data collected at discrete times from a likelihood which is conjugate to the marginal l…
BNPdensity: Bayesian nonparametric mixture modeling in R
Julyan Arbel, Guillaume Kon Kam King, Antonio Lijoi +2
Robust statistical data modelling under potential model mis-specification often requires leaving the parametric world for the nonparametric. In the latter, parameters are infinite…
Exact inference for a class of non-linear hidden Markov models on general state spaces
Guillaume Kon Kam King, Omiros Papaspiliopoulos, Matteo Ruggiero
Exact inference for hidden Markov models requires the evaluation of all distributions of interest - filtering, prediction, smoothing and likelihood - with a finite computational ef…
MOSAIC_SSD: a new web-tool for the Species Sensitivity Distribution, allowing to include censored data by maximum likelihood
Guillaume Kon Kam King, Philippe Veber, Sandrine Charles +1
Censored data are seldom taken into account in Species Sensitivity Distribution (SSD) analysis. However, they are found in virtually every dataset and sometimes represent the bette…