We develop an exact direct-sampling framework for Bayesian inverse problems whose bounded forward observables can be queried through Bernoulli events rather than numerically evaluated. A Bernstein--Poisson construction converts these events into scaled Gaussian likelihood coins. Thinning a prior-based Poisson point pro...
This work proves the $L_2$-consistency of the derivative estimator under mild regularity conditions on the densities and their domain and reformulates this optimization via a derivative condition, whose zero locates the optimal mixture parameter, and estimates the derivative directly using a-nearest-neighbor method.
Between the classical (frequentist) approach, which is based solely on the data, and a fully Bayesian set-up where one assumes a prior distribution for the model parameters, lies the Empirical Bayes (EB) approach which appears to be a good compromise between the aforementioned two approaches. Even though many researche...
Q. Nguyen, Nabendu Pal, Vinh Van Dang· 0 citations
We discuss Bayesian inference on a low-dimensional targeted parameter in the presence of possibly highly complex nuisance components within the semi-parametric inference framework using an estimating function approach. We obtain a posterior distribution using non-parametric Bayesian methods through the Dirichlet proces...
Time series of random objects, such as covariance matrices, probability distributions, and functional data, call for forecasting methods that do not rely on standard arithmetic operations. We introduce geodesic exponential smoothing, a generalization of exponential smoothing to time series in Hadamard spaces: the forec...
Takuo Matsubara, Pei-Wen Jiang, W. Y. Chen et al.· 0 citations
A Bayesian extension of GPMF, referred to as Bayesian GPMF, is proposed, and an infinite mixture representation of the exponentially tilted beta (EBeta) distribution is derived and a finite approximation based on this representation is developed.
F. Sakaori, Hiroyasu Abe· 0 citations
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