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 …