In this paper we develop new Markov chain Monte Carlo schemes for the estimation of Bayesian models. One key feature of our method, which we call the tailored randomized block Metropolis-Hastings (TaRB-MH) method, is the random clustering of the parameters at every iteration into an arbitrary number of blocks. Then each block is sequentially updated through an M-H step. Another feature is that the proposal density for each block is tailored to the location and curvature of the target density based on the output of simulated annealing, following Greenberg (1994, 1995) and Chib and Ergashev (2008). We also provide an extended version of our method for sampling multi-modal distributions in which at a pre-specified mode jumping iteration, a single-block proposal is generated from one of the modal regions using a mixture proposal density, and this proposal is then accepted according to an M-H probability of move. At the non-mode jumping iterations, the draws are obtained by applying the TaRB-MH algorithm. We also discuss how the approaches of Chib (1995) and Chib and Jeliazkov (2001) can be adapted to these sampling schemes for estimating the model marginal likelihood. The methods are illustrated in several problems. In the DSGE model of Smets and Wouters (2007), for example, which involves a 36-dimensional posterior distribution, we show that the autocorrelations of the sampled draws from the TaRB-MH algorithm decay to zero within 30-40 lags for most parameters. In contrast, the sampled draws from the random-walk M-H method, the algorithm that has been used to date in the context of DSGE models, exhibit significant autocorrelations even at lags 2500 and beyond. Additionally, the RW-MH does not explore the same high density regions of the posterior distribution as the TaRB-MH algorithm. Another example concerns the model of An and Schorfheide (2007) where the posterior distribution is multi-modal. While the RW-MH algorithm is unable to jump from the low modal region to the high modal region, and vice-versa, we show that the extended TaRB-MH method explores the posterior distribution globally in an efficient manner.

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This paper considers the use of lock-free shared objects within hard r eal-time systems. As the name suggests, lock-free shared objects are distinguished by the fact that they are not locked. As such, they do not give rise to priority inversions, a key advantage over conventional, lock-based object-sharing approaches. Despite this advantage, it is not immediately apparent that lock-free shared objects can be employed if tasks must adhere to strict timing constraints. In particular, lock-free object implementations permit concurrent operations to interfere with each other, and repeated interferences can cause a given operation to take an arbitrarily long time to complete.The main contribution of this paper is to show that such interferences can be b ounded by judicious scheduling. This work pertains to periodic, hard r eal-time tasks that share l o ck-free objects on a uniprocessor. In the rst part of the paper, scheduling conditions are derived for such tasks, for both static and dynamic priority schemes. Based on these conditions, it is formally shown that lock-free object-sharing approaches can be expected to incur much less overhead than approaches based on wait-free objects or lock-based schemes. In the last part of the paper, this conclusion is validated experimentally through work involving a realtime desktop videoconferencing system.

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This paper deals with Dynamic Stochastic General Equilibrium (DSGE) models under a multivariate student-t distribution for the structural shocks. Based on the solution algorithm of Klein (2000) and the gamma-normal representation of the t -distribution, the TaRB-MH algorithm of Chib and Ramamurthy (2010) is used to estimate the model. A technique for estimating the marginal likelihood of the DSGE student-t model is also provided. The methodologies are illustrated first with simulated data and then with the DSGE model of Ireland (2004) where the results support the t -error model in relation to the Gaussian model.