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In this paper we consider Foster-Lyapunov type drift conditions for Markov chains which imply polynomial rate convergence to stationarity in appropriate V-norms. We also show how these results can be used to prove Central Limit Theorems for functions of the Markov chain. Examples are considered to random walks on the half line and the independence sampler.
Scaling of proposals for Metropolis algorithms is an important practical problem in MCMC implementation. Criteria for scaling based on empirical acceptance rates of algorithms have been found to work consistently well across a broad range of problems. Essentially, proposal jump sizes are increased when acceptance rates are high and decreased when rates are(More)
Continuous-time stochastic volatility models are becoming an increasingly popular way to describe moderate and high-frequency financial data. Recently, Barndorff-Nielsen and Shephard (2001a) proposed a class of models where the volatility behaves according to an Ornstein-Uhlenbeck process, driven by a positive Lévy process without Gaussian component. These(More)
—Africa is the single largest continental source of bio-mass burning emissions and one where emission source strengths are characterized by strong diurnal and seasonal cycles. This paper describes the development of a fire detection and character-to 50% due to the nondetection of many low-intensity fire pixels (FRP < 50 MW). Frequency-magnitude analysis can(More)
Satellite-based remote sensing of active fires is the only practical way to consistently and continuously monitor diurnal fluctuations in biomass burning from regional, to continental, to global scales. Failure to understand, quantify, and communicate the performance of an active fire detection algorithm, however, can lead to improper interpretations of the(More)
We present and study a Langevin MCMC approach for sampling nonlinear diffusion bridges. The method is based on recent theory concerning stochastic partial differential equations (SPDEs) reversible with respect to the target bridge, derived by applying the Langevin idea on the bridge pathspace. In the process, a Random-Walk Metropolis algorithm and an(More)
We consider the problem of optimal scaling of the proposal variance for multidimensional Random walk Metropolis (RWM) algorithms. It is well known, for a wide range of continuous target densities , that the optimal scaling of the proposal variance leads to an average acceptance rate of 0.234. Therefore a natural question is, do similar results for target(More)