Lévy state-space models for tracking and intent prediction of highly maneuverable objects
In this article, we present a Bayesian framework for maneuvering object tracking and intent
prediction using novel α-stable Lévy state-space models, expressed in continuous time as …
prediction using novel α-stable Lévy state-space models, expressed in continuous time as …
Modelling impulsive noise in indoor powerline communication systems
Powerline communication (PLC) is an emerging technology that has an important role in
smart grid systems. Due to making use of existing transmission lines for communication …
smart grid systems. Due to making use of existing transmission lines for communication …
Point process simulation of generalised hyperbolic Lévy processes
Generalised hyperbolic (GH) processes are a class of stochastic processes that are used to
model the dynamics of a wide range of complex systems that exhibit heavy-tailed behavior …
model the dynamics of a wide range of complex systems that exhibit heavy-tailed behavior …
Bayesian inference for amplitude distribution with application to radar clutter
The performance of telecommunication systems is significantly subject to scattered signals
superposed at the receivers. Notably, if the superposed scattered signals are impulsive in …
superposed at the receivers. Notably, if the superposed scattered signals are impulsive in …
The Lévy state space model
In this paper we introduce a new class of state space models based on shot-noise
simulation representations of nonGaussian Lévy-driven linear systems, represented as …
simulation representations of nonGaussian Lévy-driven linear systems, represented as …
[PDF][PDF] Bibliography on stable distributions, processes and related topics
The following sections are a start on organizing references on stable distributions by topic. It
is far from complete. Starting on page 23 there is an extensive list of papers, most on stable …
is far from complete. Starting on page 23 there is an extensive list of papers, most on stable …
Numerical aspects of shot noise representation of infinitely divisible laws and related processes
The ever-growing appearance of infinitely divisible laws and related processes in various
areas, such as physics, mathematical biology, finance and economics, has fuelled an …
areas, such as physics, mathematical biology, finance and economics, has fuelled an …
Posterior inference on shallow infinitely wide Bayesian neural networks under weights with unbounded variance
From the classical and influential works of Neal (1996), it is known that the infinite width
scaling limit of a Bayesian neural network with one hidden layer is a Gaussian process …
scaling limit of a Bayesian neural network with one hidden layer is a Gaussian process …
Particle Gibbs for likelihood-free inference of stochastic volatility models
Stochastic volatility models (SVMs) are widely used in finance and econometrics for
analyzing and interpreting volatility. Real financial data are often observed to have heavy …
analyzing and interpreting volatility. Real financial data are often observed to have heavy …
Nonasymptotic Gaussian approximation for inference with stable noise
The results of a series of theoretical studies are reported, examining the convergence rate
for different approximate representations of α-stable distributions. Although they play a key …
for different approximate representations of α-stable distributions. Although they play a key …