Author | : Dag Bjarne Tjostheim,Hakon Otneim,Bard Stove |

Publisher | : Academic Press |

Release Date | : 2021-11-15 |

ISBN 10 | : 9780128158616 |

Pages | : 352 pages |

Statistical Modeling using Local Gaussian Approximation extends powerful characteristics of the Gaussian distribution - perhaps the most well-known and most used distribution in statistics - to a large class of non-Gaussian and nonlinear situations through local approximation. This extension enables the reader to follow new methods in assessing conditional distribution functions, conditional mean functions and conditional quantile functions. Three R packages are integrated with the text, based on local Gaussian correlation, density and conditional density estimation, and local spectral analysis. The book is of particular relevance and interest to researchers in econometrics and financial econometrics. Reviews local dependence modelling with applications to time series and finance markets Introduces new techniques for density estimation, conditional density estimation and tests of conditional independence with applications in economics Evaluates local spectral analysis, discovering hidden frequencies in extremes and hidden phase differences Integrates textual content with three useful R packages

Author | : Ansgar Steland,Ewaryst Rafajłowicz,Krzysztof Szajowski |

Publisher | : Springer |

Release Date | : 2015-02-04 |

ISBN 10 | : 3319138812 |

Pages | : 492 pages |

This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design, and stochastic models in engineering. Stochastic models and related statistical procedures play an important part in furthering our understanding of the challenging problems currently arising in areas of application such as the natural sciences, information technology, engineering, image analysis, genetics, energy and finance, to name but a few. This collection arises from the 12th Workshop on Stochastic Models, Statistics and Their Applications, Wroclaw, Poland.

Author | : Ayman El-Baz,Xiaoyi Jiang,Jasjit S. Suri |

Publisher | : CRC Press |

Release Date | : 2016-11-17 |

ISBN 10 | : 1482258560 |

Pages | : 526 pages |

As one of the most important tasks in biomedical imaging, image segmentation provides the foundation for quantitative reasoning and diagnostic techniques. A large variety of different imaging techniques, each with its own physical principle and characteristics (e.g., noise modeling), often requires modality-specific algorithmic treatment. In recent years, substantial progress has been made to biomedical image segmentation. Biomedical image segmentation is characterized by several specific factors. This book presents an overview of the advanced segmentation algorithms and their applications.

Author | : Tshilidzi Marwala,Ilyes Boulkaibet,Sondipon Adhikari |

Publisher | : John Wiley & Sons |

Release Date | : 2016-09-23 |

ISBN 10 | : 111915300X |

Pages | : 248 pages |

Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering Tshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South Africa Sondipon Adhikari, Swansea University, UK Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering. The book covers probabilistic finite element model updating, achieved using Bayesian statistics. The Bayesian framework is employed to estimate the probabilistic finite element models which take into account of the uncertainties in the measurements and the modelling procedure. The Bayesian formulation achieves this by formulating the finite element model as the posterior distribution of the model given the measured data within the context of computational statistics and applies these in aeronautical and mechanical engineering. Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering. Key features: Contains several contributions in the area of model updating using Bayesian techniques which are useful for graduate students. Explains in detail the use of Bayesian techniques to quantify uncertainties in mechanical structures as well as the use of Markov Chain Monte Carlo techniques to evaluate the Bayesian formulations. The book is essential reading for researchers, practitioners and students in mechanical and aerospace engineering.

Author | : Kshitij Tiwari,Nak-Young Chong |

Publisher | : Academic Press |

Release Date | : 2019-11 |

ISBN 10 | : 0128176075 |

Pages | : 290 pages |

Multi-robot Exploration for Environmental Monitoring: The Resource Constrained Perspective provides readers with the necessary robotics and mathematical tools required to realize the correct architecture. The architecture discussed in the book is not confined to environment monitoring, but can also be extended to search-and-rescue, border patrolling, crowd management and related applications. Several law enforcement agencies have already started to deploy UAVs, but instead of using teleoperated UAVs this book proposes methods to fully automate surveillance missions. Similarly, several government agencies like the US-EPA can benefit from this book by automating the process. Several challenges when deploying such models in real missions are addressed and solved, thus laying stepping stones towards realizing the architecture proposed. This book will be a great resource for graduate students in Computer Science, Computer Engineering, Robotics, Machine Learning and Mechatronics. Analyzes the constant conflict between machine learning models and robot resources Presents a novel range estimation framework tested on real robots (custom built and commercially available)

Author | : N.A |

Publisher | : N.A |

Release Date | : 2004 |

ISBN 10 | : |

Pages | : 329 pages |

Author | : N.A |

Publisher | : N.A |

Release Date | : 1999 |

ISBN 10 | : |

Pages | : 329 pages |

Author | : Kenneth Berk,Linda Malone,Terence M. Mulligan |

Publisher | : N.A |

Release Date | : 1989 |

ISBN 10 | : |

Pages | : 619 pages |

Author | : N.A |

Publisher | : N.A |

Release Date | : 2008 |

ISBN 10 | : |

Pages | : 329 pages |

Author | : N.A |

Publisher | : N.A |

Release Date | : 1992 |

ISBN 10 | : |

Pages | : 329 pages |