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A Bayesian Inference for the Extended Skew-Normal Measurement Error Model。
Bayesian Inference for the Skewness Parameter of the Scalar Skew-Normal Distribution。
We use rescaled Gaussian processes as prior models for functional parameters in nonparametric statistical models. We show how the rate of contraction of the posterior distributions depends on the scal...
We study the rate of Bayesian consistency for hierarchical priors consisting of prior weights on a model index set and a prior on a density model for each choice of model index. Ghosal, Lember and Van...
Given i.i.d. data from an unknown distribution, we consider the problem of predicting future items. An adaptive way to estimate the probability density is to recursively subdivide the domain to an a...
Stochastic volatility (SV) models provide useful tools to describe the evolution of asset returns, which exhibit time-varying volatility. This paper extends a basic SV model to capture a leverage effe...
In this paper the Bayesian analysis is applied to assign a probability density to the value of a quantity having a definite sign. This analysis is logically consistent with the results, positive or ...
Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian statespace models. We address here the case where the noise probability density functions are of unknown functi...

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