State-space models as an important mathematical tool has been widely used in many different fields. This edited collection explores recent theoretical developments of the models and their applications in economics and finance. The book includes nonlinear and non-Gaussian time series models, regime-switching and hidden Markov models, continuous- or discrete-time state processes, and models of equally-spaced or irregularly-spaced (discrete or continuous) observations. The contributed chapters are divided into four parts. The first part is on Particle Filtering and Parameter Learning in...
State-space models as an important mathematical tool has been widely used in many different fields. This edited collection explores recent theoreti...
State-space models as an important mathematical tool has been widely used in many different fields. This edited collection explores recent theoretical developments of the models and their applications in economics and finance. The book includes nonlinear andnon-Gaussian time series models, regime-switching and hidden Markov models, continuous- or discrete-time state processes, and models of equally-spaced or irregularly-spaced (discrete or continuous) observations.The contributed chapters are divided into four parts. The first part is on Particle Filtering and Parameter Learning in...
State-space models as an important mathematical tool has been widely used in many different fields. This edited collection explores recent theoreti...
This book presents two collaborative prediction approaches based on contextual representation and hierarchical representation, and their applications including context-aware recommendation, latent collaborative retrieval and click-through rate prediction.
This book presents two collaborative prediction approaches based on contextual representation and hierarchical representation, and their applications ...
