A computationally efficient method for online joint state inference and dynamical model learning is presented. The combines an a priori known, physically derived, state-space with radial basis function expansion representing unknown system dynamics inherits properties from both physical data-driven modeling. uses extended Kalman filter approach to jointly estimate the of learn dynamics, via par...

This study evaluates the performance of three alternative models for forecasting daily interbank exchange rate of U.S. dollar measured in Pak rupees. The simple ARIMA models and complex models such as GARCH-type models and a state space model are discussed and compared. Four different measures are used to evaluate the forecasting accuracy. The main result is the state space model provides the b...

High-level formalisms such as stochastic Petri nets can be used to model complex systems. Analysis of logical and numerical properties of these models often requires the generation and storage of the entire underlying state space. This imposes practical limitations on the types of systems which can be modeled. Because of the vast amount of memory consumed, we investigate distributed algorithms ...

With the influx of complex and detailed tracking data gathered from electronic tracking devices, the analysis of animal movement data has recently emerged as a cottage industry amongst biostatisticians. New approaches of ever greater complexity are constantly being added to the literature. In this paper, we review what we believe to be some of the most popular and most useful classes of statist...

this paper aims to investigate a new and intricate behavior of immediate follower during the lane change of leader vehicle. accordingly, the mentioned situation is a transient state in car following behavior during which the follower vehicle considerably deviates from conventional car following models for a limited time, which is a complex state including lateral and longitudinal movement simul...

State space models, also termed dynamic models, relate observations yt; t = 1; 2; :::, on a response variable Y to unobserved "states" or "parameters" t, t=1,2,..., by an observation model for yt given t. The states are assumed to follow a Markovian transition model. Gaussian linear state space models are de ned by a linear observation model and a linear Markovian transition equation yt = z 0 t...

For concertgoers, musical interpretation is the most important factor in determining whether or not we enjoy a classical performance. Every performance includes mistakes—intonation issues, lost note, an unpleasant sound—but these are all easily forgotten (or unnoticed) when performer engages her audience, imbuing piece with novel emotional content beyond vague instructions inscribed on printed ...