Brag Meeting 27th September 2018

When: Thursday 27th September 2018, 13:00-14:00.

Location: GP-Y801.

Included in meeting: 

The fortnightly BRAG meeting will be held tomorrow 27th September 2018 at 1pm in room Y801. This week we will have presentations by Alan and Ziwen.

Alan’s Talk:

Title:  SSNdesign — an R package for optimal and adaptive experimental design on stream networks

Abstract: Optimal experimental designs maximise the information gained from limited samples. Optimal designs are paramount when precise predictions or parameter estimates are required but data collection is resource intensive. R packages exist to find optimal designs for a few settings; e.g. AlgDesign and OPDOE. However, to our knowledge, there are no R packages for optimal design problems for stream and river networks. Stream networks provide a unique design challenge due to their branching structure and flow accumulation as water moves downstream. Given these statistical challenges and the importance of healthy freshwater ecosystems, computational tools for designing effective monitoring programs on streams with minimal cost for maximum impact are sorely needed. Here, we present SSNdesign; an R package for finding optimal designs on stream networks. This package relies on the S4 SpatialStreamNetwork object and models implemented in the package SSN. It has functionality for finding optimal designs for estimating model parameters and making predictions on stream networks. Users can also define utility functions for their own design problems.

Ziwen’s Talk:

Title: Robust Bayesian Synthetic Likelihood via a Semi-Parametric Approach

Abstract: Bayesian synthetic likelihood (BSL) is now a well established method for performing approximate Bayesian parameter estimation for simulation-based models that do not possess a tractable likelihood function.  However,  despite several successful applications of BSL, its widespread use in scientific fields may be hindered by the strong normality assumption.  In this paper, we develop a semi-parametric approach to relax this assumption to an extent and maintain the computational advantages of BSL without any additional tuning.

We look forward to seeing you all there!


Farzana and Trish


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