Bayesian high-dimensional semi-parametric inference beyond sub-Gaussian errors
نویسندگان
چکیده
منابع مشابه
Bayesian Nonparametric and Parametric Inference
This paper reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.
متن کاملBeyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs
We consider the problem of high-dimensional structured estimation with norm-regularized estimators, such as Lasso, when the design matrix and noise are drawn from sub-exponential distributions. Existing results only consider sub-Gaussian designs and noise, and both the sample complexity and non-asymptotic estimation error have been shown to depend on the Gaussian width of suitable sets. In cont...
متن کاملApplied Bayesian Non- and Semi-parametric Inference using DPpackage
Inmany practical situations, a parametric model cannot be expected to describe in an appropriate manner the chance mechanism generating an observed dataset, and unrealistic features of some common models could lead to unsatisfactory inferences. In these cases, we would like to relax parametric assumptions to allow greater modeling flexibility and robustness against misspecification of a paramet...
متن کاملBayesian inference for Gaussian graphical models beyond decomposable graphs
Bayesian inference for graphical models has received much attention in the literature in recent years. It is well known that when the graph G is decomposable, Bayesian inference is significantly more tractable than in the general non-decomposable setting. Penalized likelihood inference on the other hand has made tremendous gains in the past few years in terms of scalability and tractability. Ba...
متن کاملAdvanced mixtures for complex high dimensional data: from model-based to Bayesian non-parametric inference
Cluster analysis of complex data is an essential task in statistics and machine learning. One of the most popular approaches in cluster analysis is the one based on mixture models. It includes mixture-model based clustering to partition individuals or possibly variables into groups, block mixture-model based clustering to simultaneously associate individuals and variables to clusters, that is c...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Korean Statistical Society
سال: 2020
ISSN: 1226-3192,2005-2863
DOI: 10.1007/s42952-020-00091-4