<XML><RECORDS><RECORD><REFERENCE_TYPE>0</REFERENCE_TYPE><REFNUM>8906</REFNUM><AUTHORS><AUTHOR>Girolami,M.</AUTHOR></AUTHORS><YEAR>2008</YEAR><TITLE>Bayesian Inference for Differential Equations</TITLE><PLACE_PUBLISHED>Theoretical Computer Science</PLACE_PUBLISHED><PUBLISHER>Elsevier Science</PUBLISHER><LABEL>Girolami:2008:8906</LABEL><ABSTRACT>Nonlinear dynamic systems such as biochemical pathways can be represented in abstract form using a number of modeling formalisms. In particular differential equations provide a highly expressive mathematical framework with which to model dynamic systems, and a very natural way to model the dynamics of a biochemical pathway in a deterministic manner is through the use of nonlinear ordinary or time delay differential equations. However if, for example, we consider a biochemical pathway the constituent chemical species and hence the pathway structure is seldom fully characterised. In addition it is often impossible to obtain values of the rates of activation or decay which form the free parameters of the mathematical model. The system model in many cases is therefore not fully characterised either in terms of structure or the values which parameters take. This uncertainty must be accounted for in a systematic manner when the model is used in simulation or predictive mode to safeguard against reaching conclusions about system characteristics that are unwarranted or in making predictions that are unjustifiably optimistic given the uncertainty about the model. The Bayesian inferential methodology provides a coherent framework with which to characterise and propagate uncertainty in such mechanistic models and this paper provides an introduction to Bayesian methodology as applied to system models represented as differential equations.</ABSTRACT></RECORD></RECORDS></XML>