Learning Hawkes Processes from a Handful of Events
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We study a new image sensor that is reminiscent of traditional photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity. To analyze its performance, we formulate the oversam ...
We consider the problem of distributed estimation, where a set of nodes is required to collectively estimate some parameter of interest from noisy measurements. The problem is useful in several contexts including wireless and sensor networks, where scalabi ...
We have developed a model for pedestrian walking behavior, based on discrete choice modeling. The model is estimated by maximum likelihood estimation on a real data set of pedestrian trajectories, manually tracked from video sequences filmed in Japan. The ...
In this thesis, we focus on Impulse Radio (IR) Ultra-WideBand (UWB) ranging and positioning techniques under indoor propagation environments. IR-UWB, a new carrierless communication scheme using impulses, is a candidate technology for future communication, ...
We pose the estimation of the parameters of multiple superimposed exponential signals in additive Gaussian noise problem as a Maximum Likelihood (ML) estimation problem. The ML problem is very non linear and hard to solve. Some previous works focused on fi ...
Loss tomography aims at inferring the loss rate of links in a network from end-to-end measurements. Previous work in [1] has developed optimal maximum likelihood estimators (MLEs) for link loss rates in a single-source multicast tree. However, only sub-opt ...
Powerful mathematical tools have been developed for trading in stocks and bonds, but other markets that are equally important for the globalized world have to some extent been neglected. We decided to study the shipping market as an new area of development ...
We consider the problem of distributed estimation in adaptive networks where a collection of nodes are required to estimate in a collaborative manner some parameter of interest from their measurements. The centralized solution to the problem uses a fusion ...
We study the problem of distributed state-space estimation, where a set of nodes are required to estimate the state of a nonlinear state-space system based on their observations. We extend our previous work on distributed Kalman filtering to the nonlinear ...
We propose a high-resolution ranging algorithm for impulse radio (IR) ultra-WideBand (UWB) communication systems in additive white Gaussian noise. We formulate the ranging problem as a maximum- likelihood (ML) estimation problem for the channel delays and ...
Institute of Electrical and Electronics Engineers2009