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Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].

This is a review of the theoretical aspects of the supersymmetric extension of the Standard Model of particle physics, extracted from Chapter 87 of the 2026 Review of Particle Physics, F. Takahashi et al. (Particle Data Group), Int. J. Mod.…

We commend the authors for an exciting paper which provides a strong contribution to the emerging field of probabilistic numerics (PN). Below, we discuss aspects of prior modelling which need to be considered thoroughly in future work.

In this rejoinder we summarize the comments, questions and remarks on the paper "A novel algorithmic approach to Bayesian Logic Regression" from the discussants. We then respond to those comments, questions and remarks, provide several…

The intersection set of Bayesian and nonparametric statistics was almost empty until about 1973, but now is growing at a healthy rate. This chapter, for the {\it Highly Structured Stochastic Systems} book (Oxford University Press, 2003)…

We analyze in great detail the mathematical derivations from the article "Doppler signature in electrodynamic retarded potentials" by Giovanni Perosa, Simone Di Mitri, William A. Barletta, and Fulvio Parmigiani, published in Physics Open…

This is a technical report, containing all the theorem proofs and additional evaluations in paper "Monitor Placement for Maximal Identifiability in Network Tomography" by Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, Don Towsley,…

We warmly thank Kosuke Imai, Michael Lingzhi Li, and Stefan Wager for their gracious and insightful comments. We are particularly encouraged that both pieces recognize the importance of the research agenda the lecture laid out, which we see…

We would like to congratulate the authors of "A Bayesian Conjugate Gradient Method" on their insightful paper, and welcome this publication which we firmly believe will become a fundamental contribution to the growing field of probabilistic…

Comment: Bayesian Checking of the Second Levels of Hierarchical Models [arXiv:0802.0743]

Comment: Bayesian Checking of the Second Levels of Hierarchical Models [arXiv:0802.0743]

We comment on the latest paper [K.-H. Ding, Z.-G. Zhu, and J. Berakdar, J. Phys.: Condens. Matter 24, 266003 (2012)].

In this paper we review recently developed methods for nonparametric Bayesian inference for one-dimensional diffusion models. We discuss different possible prior distributions, computational issues, and asymptotic results.

Discussion paper on "Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing" by Wand [arXiv:1602.07412].

We reply to Dukelsky, et al. regarding the article: L. A. Wu, M. S. Byrd and D. A. Lidar, Phys. Rev. Lett. 89, 057904 (2002).

Rejoinder to "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].

A comment to the paper by S. Chen, H. B\"uttner, and J. Voit, [Phys. Rev. Lett. {\bf 87}, 087205 (2001)].

Discussion on "Brownian distance covariance" by G\'{a}bor J. Sz\'{e}kely, Maria L. Rizzo [arXiv:1010.0297]

Discussion on "Brownian distance covariance" by G\'{a}bor J. Sz\'{e}kely, Maria L. Rizzo [arXiv:1010.0297]

This Comment points out a number of errors in the recent paper by Zarechnaya, Dubrovinskaia, Dubrovinsky, et al. (Phys. Rev. Lett. 102, 185501 (2009)). Results and conclusions presented by Zarechnaya et al. (2009) are either incorrect or…