Descargar Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods en PDF de James V. Candy

Descargar Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods en PDF de James V. Candy año 2009

Ficha completa del libro


  • Nombre del libro: Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods
  • Autor del libroJames V. Candy
  • Fecha de publicación: 23/9/2009
  • EditorialLEA
  • Páginas del PDF: 472
  • IdiomaInglés
  • Incluye un resumen PDF de 45 páginas
  • ISBN: 9780470180945
  • Encuadernación: Tapa Dura
  • Género o ColecciónIngeniería
  • Valoración del libro: 4.92 de un máximo de 5
  • Votos: 51 
  • Descripción o resumen: New Bayesian approach helps you solve tough problems in signal processing with ease. Signal processing is based on this fundamental concept—the extraction of critical information from noisy, uncertain data. Most techniques rely on underlying Gaussian assumptions for a solution, but what happens when these assumptions are erroneous? Bayesian techniques circumvent this limitation by offering a completely different approach that can easily incorporate non-Gaussian and nonlinear processes along with all of the usual methods currently available. This text enables readers to fully exploit the many advantages of the "Bayesian approach" to model-based signal processing. It clearly demonstrates the features of this powerful approach compared to the pure statistical methods found in other texts. Readers will discover how easily and effectively the Bayesian approach, coupled with the hierarchy of physics-based models developed throughout, can be applied to signal processing problems that previously seemed unsolvable. Bayesian Signal Processing features the latest generation of processors (particle filters) that have been enabled by the advent of high-speed/high-throughput computers. The Bayesian approach is uniformly developed in this book's algorithms, examples, applications, and case studies. Throughout this book, the emphasis is on nonlinear/non-Gaussian problems; however, some classical techniques (e.g. Kalman filters, unscented Kalman filters, Gaussian sums, grid-based filters, et al) are included to enable readers familiar with those methods to draw parallels between the two approaches.
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Autor de la reseña


  • Subido por: Pastora Saenz
  • Fecha: 19/6/2018
  • Valorado con una puntuación de 4.97 de un máximo de 5
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