The initial number of susceptible individuals in a population is usually assumed to be known and statistical inference for some of the quantities of interest, such as the basic reproductive number R0, is straightforward. However, in any epidemic, there may exist a number of individuals who may...

type="main" xml:id="rssa12044-abs-0001" <title type="main">Summary</title> <p>The paper proposes a framework for modelling financial contagion that is based on susceptible–infected–recovered transmission models from epidemic theory. This class of models addresses two important features of contagion modelling, which are a...</p>

Inference for Dirichlet process hierarchical models is typically performed using Markov chain Monte Carlo methods, which can be roughly categorized into marginal and conditional methods. The former integrate out analytically the infinite-dimensional component of the hierarchical model and sample...

We discuss a formal mathematical framework for certain coupling constructions via minorisation conditions, which are often used to prove bounds on convergence to stationarity of stochastic processes and Markov chain Monte Carlo algorithms.

We consider Markov chains {[Gamma]n} with transitions of the form [Gamma]n=f(Xn,Yn)[Gamma]n-1+g(Xn,Yn), where {Xn} and {Yn} are two independent i.i.d. sequences. For two copies {[Gamma]n} and {[Gamma]n'} of such a chain, it is well known that provided E[log(f(Xn,Yn))]<0, where => is weak convergence. In...</0,>

A Bayesian perspective is taken to quantify the amount of information learned from observing a stochastic process, Xt, on the interval [0, T] which satisfies the stochastic differential equation, dXt = S([theta], t, Xt)dt+[sigma](t, Xt)dBt. Information is defined as a change in expected utility...