Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …

Sensitivity analysis is an important part of a mathematical modeller's toolbox for model
analysis. In this review paper, we describe the most frequently used sensitivity techniques …

Heuristically, a discrete-time stochastic process has the Markov property if the past and
future are independent given the present. In this introductory chapter, we give the formal …

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines
the Bayesian and frequentist methods of conducting data analyses. The book provides the …

Graphical models provide visual representations of the qualitative structure of our beliefs
between collections of random quantities. Bayesian Networks are directed acyclic graphical …

This new edition updates Durbin & Koopman's important text on the state space approach to
time series analysis. The distinguishing feature of state space time series models is that …

Real world entities work in concert as a system and not in isolation. Understanding the
associations between these entities from their digital signatures can provide novel system …

Statistical pattern recognition is a very active area of study andresearch, which has seen
many advances in recent years. New andemerging applications-such as data mining, web …

Bayesian Statistics: Page 1 Bayesian Statistics: An Introduction PETER M. LEE Formerly Provost
of Wentworth College, University of York, England Fourth Edition John Wiley & Sons, Ltd Page …

The volatility of financial returns changes over time and, for the last thirty years, Generalized
Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal …