Kalman-Bucy smoothers are often used to estimate the state variables as a function of time in a system with stochastic dynamics and measurement noise. This is accomplished using an algorithm for which the number of numerical operations grows linearly with the number of time points. All of the randomness in the model is assumed to be Gaussian. Including other available information, for example a...

Kluver-Bucy syndrome is an uncommon syndrome of behavioral abnormalities following bilateral temporal lobe injury. Only four children have been reported previously with this syndrome. We report three additional pediatric patients who developed Kluver-Bucy syndrome following hypoxic insults. In two patients, features of the syndrome were transient. Problems in intermediate memory were present in...

In the era of precision medicine, the impact of personalized dosing of busulfan is not clear. We undertook a retrospective analysis of 78 patients with myeloid malignancies who received fludarabine and busulfan (FluBu4) with or without measuring Bu pharmacokinetics (Bu PK) and those who received busulfan with cyclophosphamide (BuCy). Fifty-five patients received FluBu4, of whom 21 had Bu PK mea...

Abstract In this article we consider the estimation of log-normalization constant associated to a class continuous-time filtering models. particular, ensemble Kalman–Bucy filter estimates based upon several nonlinear diffusions. Using new conditional bias results for mean aforementioned methods, analyze empirical log-scale normalization constants in terms their $\mathbb{L}_n$ -errors and -condi...

The nonlinear filtering problem of estimating the state of a linear stochastic system from noisy observations is solved for a broad class of probability distributions of the initial state. It is shown that the conditional density of the present state, given the past observations, is a mixture of Gaussian distributions, and is parametrically determined by two sets of sufficient statistics which ...

This paper proposes a sampling based kinodynamic planning technique for planning informative trajectories for a sensing robot in a spatiotemporal environmental field. The robot uses a Kalman-Bucy filter to estimate the spatiotemporal field. The proposed algorithm finds a trajectory for the robot that minimizes a metric on the error covariance matrix of the field estimate. Since the error covari...