216,430 research outputs found
Heritage Society (Houston)
No full textTranscript of Letter from J. Gibbs to William M. Rice discussing taxes due to the county on his assessed properties
Heritage Society (Houston)
No full textLetter from J. Gibbs to William M. Rice discussing taxes due to the county on his assessed properties
Adaptive Gibbs samplers
No full textWe consider various versions of adaptive Gibbs and Metropolis-
within-Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the
fly during a run, by learning
as they go in an attempt to optimise the algorithm. We present a cautionary
example of how even a simple-seeming adaptive Gibbs sampler may fail to
converge. We then present various positive results guaranteeing convergence
of adaptive Gibbs samplers under certain conditions
Adaptive Gibbs samplers and related MCMC methods
No full textWe consider various versions of adaptive Gibbs and Metropolis-
within-Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the
y during a run, by learning
as they go in an attempt to optimise the algorithm.We present a cautionary
example of how even a simple-seeming adaptive Gibbs sampler may fail to
converge.We then present various positive results guaranteeing convergence
of adaptive Gibbs samplers under certain conditions
Concentration Inequalities for Functions of Gibbs Fields with Application to Diffraction and Random Gibbs Measures
No full textWe derive useful general concentration inequalities for functions of Gibbs fields in the uniqueness regime. We also consider expectations of random Gibbs measures that depend on an additional disorder field, and prove concentration w.r.t. the disorder field. Both fields are assumed to be in the uniqueness regime, allowing in particular for non-independent disorder fields. The modification of the bounds compared to the case of an independent field can be expressed in terms of constants that resemble the Dobrushin contraction coefficient, and are explicitly computable.
On the basis of these inequalities, we obtain bounds on the deviation of a diffraction pattern created by random scatterers located on a general discrete point set in Euclidean space, restricted to a finite volume. Here we also allow for thermal dislocations of the scatterers around their equilibrium positions. Extending recent results for independent scatterers, we give a universal upper bound on the probability of a deviation of the random scattering measures applied to an observable from its mean. The bound is exponential in the number of scatterers with a rate that involves only the minimal distance between points in the point set.
A partially collapsed Gibbs sampler for Bayesian quantile regression
No full textWe introduce a set of new Gibbs sampler for Bayesian analysis of quantile re-gression model. The new algorithm, which partially collapsing an ordinary Gibbs sampler, is called Partially Collapsed Gibbs (PCG) sampler. Although the Metropolis-Hastings algorithm has been employed in Bayesian quantile regression, including
median regression, PCG has superior convergence properties to an ordinary Gibbs sampler. Moreover, Our PCG sampler algorithm, which is based on a theoretic derivation of an asymmetric Laplace as scale mixtures of normal distributions,
requires less computation than the ordinary Gibbs sampler and can significantly reduce the computation involved in approximating the Bayes Factor and marginal likelihood. Like the ordinary Gibbs sampler, the PCG sample can also be used
to calculate any associated marginal and predictive distributions. The quantile regression PCG sampler is illustrated by analysing simulated data and the data of length of stay in hospital. The latter provides new insight into hospital perfor-mance. C-code along with an R interface for our algorithms is publicly available
on request from the first author.
JEL classification: C11, C14, C21, C31, C52, C53
Letter from Sandford Gibbs to O. M. Roberts
No full textA letter from Sandford Gibbs to the governor of Texas, O. M. Roberts. The letter explains that Colonel Abercrombie is in court and can not handle matters concerning the Normal School at that time. However the letter was forwarded, and Sandford Gibbs and Abercrombie promised to handle the duties required of them as soon as they were able
Letter to Dr. H. F. Estill from T. M. Gibbs
No full textT. M. Gibbs wrote a letter to H. F. Estill confirming that he received the letter that Estill had written to him. This letter talks vaguely about an issue about Teachers' Trainding Courses with A. & M. College
Gibbs sampling will fail in outlier problems with strong masking
No full textThis paper discusses the convergence of the Gibbs sampling algorithm when it is applied to the problem of outlier detection in regression models. Given any vector of initial conditions, theoretically, the algorithm converges to the true posterior distribution. However, the speed of convergence may slow down in a high dimensional parameter space where the parameters are highly correlated. We show that the effect of the leverage in regression models makes very difficult the convergence of the Gibbs sampling algorithm in sets of data with strong masking. The problem is illustrated in several examples
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