<p>Collections of large volumes of rich and complex data has become ubiquitous in recent years, posing new challenges in methodological and theoretical statistics alike. Today, statisticians are tasked with developing flexible methods capable of adapting to the degree of complexity and noise in increasingly rich data gathered across a variety of disciplines and settings. This has spurred the need for novel multivariate regression techniques that can efficiently capture a wide range of naturally occurring predictor-response relations, identify important predictors and their interactions and do so even when the number of predictors is large but the sample size remains limited. </p><p>Meanwhile, efficient model fitting tools must evolve quickl...
This thesis focuses on sources of error in modern Bayesian analysis and machine learning in the ``bi...
In this PhD thesis problems of Bayesian model selection and model averaging are addressed in various...
Abstract. This paper proposes to review some recent developments in Bayesian statistics for high dim...
<p>Many modern applications fall into the category of "large-scale" statistical problems, in which b...
Bayesian statistics has emerged as a leading paradigm for the analysis of complicated datasets and f...
<p>In this thesis, we develop some Bayesian sparse learning methods for high dimensional data analys...
Each of the three chapters included here attempts to meet a different comput-ing challenge that pres...
© 2015, The Author(s). Recent decades have seen enormous improvements in computational inference for...
This paper proposes to review some recent developments in Bayesian statistics for high dim...
This paper proposes to review some recent developments in Bayesian statistics for high dimensional d...
Recent decades have seen enormous improvements in computational inference for statistical models; th...
The availability of datasets with large numbers of variables is rapidly increasing. The effective ap...
Across the sciences, social sciences and engineering, applied statisticians seek to build understand...
Sparsity is a standard structural assumption that is made while modeling high-dimensional statistica...
This thesis is focused on the development of computationally efficient procedures for regression mod...
This thesis focuses on sources of error in modern Bayesian analysis and machine learning in the ``bi...
In this PhD thesis problems of Bayesian model selection and model averaging are addressed in various...
Abstract. This paper proposes to review some recent developments in Bayesian statistics for high dim...
<p>Many modern applications fall into the category of "large-scale" statistical problems, in which b...
Bayesian statistics has emerged as a leading paradigm for the analysis of complicated datasets and f...
<p>In this thesis, we develop some Bayesian sparse learning methods for high dimensional data analys...
Each of the three chapters included here attempts to meet a different comput-ing challenge that pres...
© 2015, The Author(s). Recent decades have seen enormous improvements in computational inference for...
This paper proposes to review some recent developments in Bayesian statistics for high dim...
This paper proposes to review some recent developments in Bayesian statistics for high dimensional d...
Recent decades have seen enormous improvements in computational inference for statistical models; th...
The availability of datasets with large numbers of variables is rapidly increasing. The effective ap...
Across the sciences, social sciences and engineering, applied statisticians seek to build understand...
Sparsity is a standard structural assumption that is made while modeling high-dimensional statistica...
This thesis is focused on the development of computationally efficient procedures for regression mod...
This thesis focuses on sources of error in modern Bayesian analysis and machine learning in the ``bi...
In this PhD thesis problems of Bayesian model selection and model averaging are addressed in various...
Abstract. This paper proposes to review some recent developments in Bayesian statistics for high dim...