We propose a new model-agnostic search strategy for physics beyond the standard model (BSM) at LHC, based on novel application of neural density estimation to anomaly detection. Our approach, which we call classifying anomalies through outer (cathode), assumes BSM signal is localized in region (defined e.g., using invariant mass). By training conditional estimator collection additional features...

Statistical process control (SPC) is widely used in industrial settings to monitor processes for shifts in their distributions. SPC is generally thought of in two distinct phases: Phase–I, in which historical data is analyzed in order to establish an in–control process, and Phase–II, in which new data is monitored for deviations from the in–control form. Traditionally, SPC had been used to moni...

A new, non–parametric and binless estimator for the mutual information of a d–dimensional random vector is proposed. First of all, an equation that links the mutual information to the entropy of a suitable random vector with uniformly distributed components is deduced. When d = 2 this equation reduces to the well known connection between mutual information and entropy of the copula function ass...

This letter considers the problem of linear time-invariant (LTI) system identification using input/output data. Recent work has provided non-asymptotic results on partially observed LTI a single trajectory but is only suitable for stable systems. We provide finite-time analysis learning Markov parameters based ordinary least-squares (OLS) estimator multiple trajectories, which covers both and u...

In this paper it is shown that the use of non-singular block invariant matrices of covariates leads tògeneralized estimating equations' estimators 13{22) which are identical regardless of thèworking' correlation matrix used. Moreover, they are eecient (McCullagh, P. (1983). The Annals of Statistics, 11(1), 59{67). If on the other hand only time invariant covari-ates are used the eeciency gain i...

This paper is concerned with a problem of robust filtering for a finite-dimensional linear discrete time invariant system with two output signals, one of which is directly observed while the other has to be estimated. The system is assumed to be driven by a random disturbance produced from the Gaussian white noise sequence by an unknown shaping filter. The worst-case performance of an estimator...

In peer-to-peer networks, free riding is a major problem. Reputation management systems can be used to overcome this problem. Reputation estimation methods generally do not consider the uncertainties in the inputs. We propose a reputation estimation method using BLUE (Best Linear Unbiased estimator) estimator that consider uncertainties in the input variables.