A dynamic modelling strategy for Bayesian computer model emulation

A Dynamic Modelling Strategy for Bayesian Computer Model Emulation

Computer model evaluation studies build statistical models of deterministic simulation-based predictions of field data to then assess and criticize the computer model and suggest refinements. Computer models are often expensive computationally: statistical models that adequately emulate their key features can be very much more efficient. Gaussian process models are often used as emulators, but ...

Bayesian Dynamic Modelling

Bayesian time series and forecasting is a very broad field and any attempt at other than a very selective and personal overview of core and recent areas would be foolhardy. This chapter therefore selectively notes some key models and ideas, leavened with extracts from a few time series analysis and forecasting examples. For definitive development of core theory and methodology of Bayesian state...

Dynamic Bayesian Networks for Brain-Computer Interfaces

We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN) can be used to infer probability distributions over brainand body-states during planning and execution of actions. The DBN is learned directly from observed data and allows measured signals such as EEG and EMG to be ...

Emulation – a viable preservation strategy

Modeling Computer Networks for Emulation

performance analysis of distributed applications and protocols. An emulation scenario consists of a network topology, link parameters, and dynamic change models. In order to set up a scenario automatically, it has to be modeled in an appropriate description language. In this paper, we analyze the requirements for an emulation scenario description language and propose a possible solution.