We describe novel Bayesian models for time-frequency inverse modelling of non-stationary signals. These models are based on the idea of a "Gabor regression", in which a time series is represented as a superposition of translated, modulated versions of a window function exhibiting good...

Prediction of future security returns is possible by decomposing a securities price into weighted superpositions of underlying basis states, given stationary distributions of the basis states. The (ensemble) Hilbert-Huang transform (HHT) is an empirical two-step online methodology which carries...

The limit order book of an exchange represents an information store of market participants' future aims and for many traders the information held in this store is of interest. However, information loss occurs between orders being entered into the exchange and limit order book data being sent...

Sequential Monte Carlo (SMC) samplers [Del Moral, P., Doucet, A., Jasra, A., 2006. Sequential Monte Carlo samplers. J. Roy. Statist. Soc. B 68, 411-436] are designed to simulate from a sequence of probability measures on a common measurable space . One way to measure the accuracy of the...

We propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space, each distribution being known up to a normalizing constant. These probability distributions are approximated by a cloud of weighted random samples which are propagated...