[Eoas-seminar] Reminder TODAY: GFDI Student Seminar - Bayesian Multimodel Analysis with Application to Microbial Soil Respiration Models

eoas-seminar at lists.fsu.edu eoas-seminar at lists.fsu.edu
Tue Apr 11 09:40:57 EDT 2017

> *GFDI Student Seminar*
> Hi GDF fellows. this coming Tuesday we have other seminar about Bayesian
> analysis and its applications. Here is the information:
> Presenter: * Ahmed Elshall*
> Topic: *Theory and Application of Bayesian Multimodel Analysis with
> Application to Microbial Soil Respiration Models*
> Place:* Melvin Stern Seminar Room, #18 Keen Bldg*
> Date: *Tuesday April 11th at 2:00 p.m.*
> Abstract:
> Models in biogeoscience are subject to parametric and conceptual
> uncertainties. To accommodate different sources of uncertainty, multimodel
> analysis such as model selection and model averaging are becoming popular.
> This talk will present the theoretical and practical challenges of Bayesian
> multimodel analysis, using a microbial soil respiration modeling example.
> We are interested in these models because global soil respiration releases
> about ten times more carbon dioxide to the atmosphere than all
> anthropogenic emissions. Improving our understanding of microbial soil
> respiration is essential for reducing the uncertainties of earth system
> models. This study focuses on a poorly understood phenomena, which is the
> soil microbial respiration pulses in response to episodic rainfall pulses,
> the “Birch effect”. The hypothesis is that the “Birch effect” is generated
> by three mechanisms that will be discussed during the talk. To test the
> hypothesis, five microbial-enzyme models were developed and assessed
> against field measurements from a semiarid Savannah that is characterized
> by pulsed precipitation. These five models evolve stepwise such that the
> first model includes none of the three mechanism, while the fifth model
> includes the three mechanisms. The first part of the talk will illustrate
> Bayesian model selection for the five models. Bayesian inference, which
> involves updating a prior parameter disruption to a posterior parameter
> distribution using a likelihood function, will be illustrated as well as
> the estimation of Bayesian model evidence for model selection purpose. The
> second part will discuss an important theoretical and practical challenge,
> which is the effect of likelihood function selection on both Bayesian model
> selection and model averaging. The talk will show that making valid
> inference from scientific data is not a trivial task, since we are not only
> uncertain about the candidate models, but also about the statistical
> techniques that are used to appraise these models.
> ​Hope to see you all there! Refreshments will be provided.​
> ___________________________________
> Roger B. Pacheco Castro
> Geophysical Fluid Dynamics Institute
> Florida State University
> *Go Noles!*
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