Related papers: Comments on: "Hybrid Semiparametric Bayesian Netwo…

This article provides a brief introduction to seven papers that are included in this special section on Statistics in Neuroscience: (1) Xiaoyan Shi, Joseph G. Ibrahim, Jeffrey Lieberman, Martin Styner, Yimei Li and Hongtu Zhu: Two-state…

This is a comment on the article mentioned in the title.

Invited discussion for Biometrika of 'Multivariate Fisher's independence test for multivariate dependence' by Gorsky and Ma (2022).

Finite mixture models have been a very important tool for exploring complex data structures in many scientific areas, for example, economics, epidemiology, finance. In the past decade, semiparametric techniques have been popularly…

We present a new approach for inference in Bayesian networks, which is mainly based on partial differentiation. According to this approach, one compiles a Bayesian network into a multivariate polynomial and then computes the partial…

The content of this paper is now available as part of arXiv:0902.1502

There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which…

This paper has been withdrawn by the author due to a crucial accuracy error in Fig. 5. For precise performance of ALBNN please refer to Yoon et al.'s work in the following article. Yoon, H., Park, C. S., Kim, J. S., & Baek, J. G. (2013).…

This article is the rejoinder for the paper "Probabilistic Integration: A Role in Statistical Computation?" to appear in Statistical Science with discussion. We would first like to thank the reviewers and many of our colleagues who helped…

A brief comment on A variational Bayesian approach for inverse problems with skew-t error distributions (Guha et al., Journal of Computational Physics 301 (2015) 377-393) is given in this letter.

The paper is being withdrawn since the authors felt that the submission is a little premature after a careful reading by some of the experts in this field.

While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning and planning require an even higher level of intelligence.…

Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to…

We comment on the recent paper by Balog and Niedermaier [hep-th/9701156].

Discussion on "Brownian distance covariance" by G\'abor J. Sz\'ekely and Maria L. Rizzo [arXiv:1010.0297]

We used the Ladder Network [Rasmus et al. (2015)] to perform Hyperspectral Image Classification in a semi-supervised setting. The Ladder Network distinguishes itself from other semi-supervised methods by jointly optimizing a supervised and…

We wish to contribute to the discussion of "Comparing Consensus Monte Carlo Strategies for Distributed Bayesian Computation" by offering our views on the current best methods for Bayesian computation, both at big-data scale and with smaller…

In this paper we introduce new models of complex weighted networks sharing several properties with fractal sets: the deterministic non-homogeneous weighted fractal networks and the stochastic weighted fractal networks. Networks of both…

Comment on "Approaching human language with complex networks" by Cong & Liu

This is a technical report, containing all the theorem proofs and additional evaluations in paper "Efficient Identification of Additive Link Metrics via Network Tomography" by Liang Ma, Ting He, Kin K. Leung, Don Towsley, and Ananthram…