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>

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...

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...

A systematic Bayesian framework is developed for physics constrained parameter inference of stochastic differential equations (SDE) from partial observations. Physical constraints are derived for stochastic climate models but are applicable for many fluid systems. A condition is derived for...

We present an approach to Bayesian model selection for finitely observed diffusion processes. We use data augmentation by treating the paths between observed points as missing data. For a fixed model formulation, the strong dependence between the missing paths and the volatility of the diffusion...

We consider the extent to which Markov chain convergence properties are affected by the presence of computer floating-point roundoff error. Both geometric ergodicity and polynomial ergodicity are considered. This paper extends previous work of Roberts et al. (J. Appl. Probab. 35 (1998) 1) to the...

We consider estimation of scalar functions that determine the dynamics of diffusion processes. It has been recently shown that nonparametric maximum likelihood estimation is ill-posed in this context. We adopt a probabilistic approach to regularize the problem by the adoption of a prior...