Search results for "Filter"
showing 10 items of 1019 documents
Parsimonious adaptive rejection sampling
2017
Monte Carlo (MC) methods have become very popular in signal processing during the past decades. The adaptive rejection sampling (ARS) algorithms are well-known MC technique which draw efficiently independent samples from univariate target densities. The ARS schemes yield a sequence of proposal functions that converge toward the target, so that the probability of accepting a sample approaches one. However, sampling from the proposal pdf becomes more computationally demanding each time it is updated. We propose the Parsimonious Adaptive Rejection Sampling (PARS) method, where an efficient trade-off between acceptance rate and proposal complexity is obtained. Thus, the resulting algorithm is f…
Conditional particle filters with diffuse initial distributions
2020
Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initial distributions, which are common in statistical applications. We propose a simple but generally applicable auxiliary variable method, which can be used together with the CPF in order to perform efficient inference with diffuse initial distributions. The method only requires simulatable Markov transitions that are reversible with respect to the initial distribution, which can be improper. We focus in particular on random-walk type transitions which are reversible with respect to a uniform init…
Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions
2021
We develop a Bayesian inference method for diffusions observed discretely and with noise, which is free of discretisation bias. Unlike existing unbiased inference methods, our method does not rely on exact simulation techniques. Instead, our method uses standard time-discretised approximations of diffusions, such as the Euler--Maruyama scheme. Our approach is based on particle marginal Metropolis--Hastings, a particle filter, randomised multilevel Monte Carlo, and importance sampling type correction of approximate Markov chain Monte Carlo. The resulting estimator leads to inference without a bias from the time-discretisation as the number of Markov chain iterations increases. We give conver…
Bootstrap validation of links of a minimum spanning tree
2018
We describe two different bootstrap methods applied to the detection of a minimum spanning tree obtained from a set of multivariate variables. We show that two different bootstrap procedures provide partly distinct information that can be highly informative about the investigated complex system. Our case study, based on the investigation of daily returns of a portfolio of stocks traded in the US equity markets, shows the degree of robustness and completeness of the information extracted with popular information filtering methods such as the minimum spanning tree and the planar maximally filtered graph. The first method performs a "row bootstrap" whereas the second method performs a "pair bo…
Panel Data Analysis via Mechanistic Models
2018
Panel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a distinct unit. Mechanistic modeling involves writing down scientifically motivated equations describing the collection of dynamic systems giving rise to the observations on each unit. A defining characteristic of panel systems is that the dynamic interaction between units should be negligible. Panel models therefore consist of a collection of independent stochastic processes, generally linked through shared parameters while also having unit-specific parameters. To give the scientist flexibility in model spe…
The invisible setting of digital space: the Facebook case
2018
La neutralidad en la red no existe. El espacio digital puede ser adaptado de forma automática al perfil de cada usuario. Sin que este tenga que identificarse, las empresas de gestión de contenidos en Internet disponen de suficientes datos de cualquier individuo para poder filtrar los resultados de su búsqueda y personalizarlos, sin previo aviso, condicionando así su experiencia en la red. Esta investigación, de carácter exploratorio, aborda en primer lugar la descripción del espacio público digital. En segundo lugar, plantea una tipología de espacios digitales que se definen en función del grado de adaptación de los contenidos al usuario. Finalmente, presenta un análisis de caso de espacio …
Testing the X-IFU calibration requirements: an example for quantum efficiency and energy resolution
2018
With its array of 3840 Transition Edge Sensors (TESs) operated at 90 mK, the X-Ray Integral Field Unit (X-IFU) on board the ESA L2 mission Athena will provide spatially resolved high-resolution spectroscopy (2.5 eV FWHM up to 7 keV) over the 0.2 to 12 keV bandpass. The in-flight performance of the X-IFU will be strongly affected by the calibration of the instrument. Uncertainties in the knowledge of the overall system, from the filter transmission to the energy scale, may introduce systematic errors in the data, which could potentially compromise science objectives - notably those involving line characterisation e.g. turbulence velocity measurements - if not properly accounted for. Defining…
Seismically induced, non-stationary hydrodynamic pressure in a dam-reservoir system
2003
Stochastic seismic analysis of hydrodynamic pressure in a dam-reservoir system is presented in this paper. The analysis is conducted assuming infinite reservoir compressible fluid and modeling seismic acceleration as a normal zero-mean stochastic process obtained by Penzien filter. The non-homogeneous boundary conditions associated to the problem have been incorporated into the equation of pressure wave scattering in the form of a forcing function turning the non-homogeneous boundary value problem into an homogeneous one. Solution obtained via modal analysis in time-domain is coupled with the use of classical Ito stochastic differential calculus to characterize the stochastic hydrodynamic p…
Optimal Flight Path Determination in Turbulent Air: A Modified EKF Approach
2017
By using the Extended Kalman Filter an accurate path following in turbulent air is performed. The procedure employs simultaneously two different EKFs: the first one estimates disturbances, the second one affords to determine the necessary controls displacements for rejecting those ones. To tune the EKFs an optimization algorithm has been designed to automatically determine Process Noise Covariance and Measurement Noise Covariance matrices. The first filter, by using instrumental measurements gathered in turbulent air, estimates wind components. The second one obtains command laws able to follow the desired flight path. To perform this task aerodynamic coefficients have been modified. Such a…
Fast Distributed Subspace Projection via Graph Filters
2018
A significant number of linear inference problems in wireless sensor networks can be solved by projecting the observed signal onto a given subspace. Decentralized approaches avoid the need for performing such an operation at a central processor, thereby reducing congestion and increasing the robustness and the scalability of the network. Unfortunately, existing decentralized approaches either confine themselves to a reduced family of subspace projection tasks or need an infinite number of iterations to obtain the exact projection. To remedy these limitations, this paper develops a framework for computing a wide class of subspace projections in a decentralized fashion by relying on the notio…