This report introduces a novel approach to performing inference and learning in Dynamic Bayesian Networks (DBN). The … traditional approach to inference and learning in DBNs involves conditioning on one or more finite-length observation sequences … solving inference problems (i.e., computing P(X|Φ)) and learning problems (i.e., computing P(Θ|Φ)) using techniques from the …

leads to biased estimates and adversely affects inference if ignored. In the literature, often convenient distribution … ignored entirely. In this thesis, ways to estimate the parameters of the Ricker model and perform inference while accounting … investigated for data on the abundance and log-abundance scales, and how inference is done via the parametric bootstrap and …

Two separate, but interlinked methodological approaches – the modelling approach and non-modelling approach – have been … approaches a range of methodological and analytical tools are used, as follows:Non-modelling Approach• Literature Review;• Case … for the case study Member States.Modelling Approach:• Budget model, tailor made for the project, provides much of the …

Presentation by Professor Louise Young of the University of Western Sydney at the Agent-Based Modelling Intensive …-based modelling and simulation will greatly assist the research into these systems. Areas covered include the dynamics and evolution …

Management (UNSW) at the Agent-Based Modelling Intensive Course Wednesday Colloquium, held at The University of Sydney Business …