Detection Method And Probabilistic Models
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Detection Method And Probabilistic Models
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Author : W. K. ESTES
language : en
Publisher:
Release Date : 1964
Detection Method And Probabilistic Models written by W. K. ESTES and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1964 with categories.
Novelty Detection For Multivariate Data Streams With Probabilistic Models
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Author : Christian Gruhl
language : de
Publisher: BoD – Books on Demand
Release Date : 2022-01-01
Novelty Detection For Multivariate Data Streams With Probabilistic Models written by Christian Gruhl and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-01 with Technology & Engineering categories.
The autonomous detection of unexpected changes in data is called novelty detection. Multivariate data streams consisting of measurements from multiple sensors often form the basis to detect such changes. Specific examples of such changes are, for instance, cardiac arrhythmias, power failures, storms or network attacks. Accordingly, changes can affect both a system itself and the environment in which it is embedded. This doctoral thesis investigates methods for online novelty detection in multivariate data streams and presents the CANDIES methodology. A unique feature of this method is the explicit separation of the input space of a probabilistic model into different regions – High-Density Regions (HDR) and Low-Density Regions (LDR) – with detection techniques specifically designed for each. While other detectors can usually only detect novelties or anomalies in LDR, the CANDIES method can also identify novelties in HDR. It also offers possibilities to handle concept drift and noise in data streams. Another distinctive feature of CANDIES is the notion of novelties as an agglomeration of anomalies that have a certain relation to each other (spatially or temporally). Additionally, the focus of this work is also on the experimental evaluation of novelty detection algorithms in general. For this purpose, a data generator that can synthesise data streams and novelties is presented, and a new evaluation measure, the FDS, is specifically designed to evaluate novelty detection methods. All methods, algorithms and tools developed and used in this thesis are also publicly and freely available online.
Probabilistic Modeling For Novelty Detection With Applications To Fraud Identification
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Author : Rémi Domingues
language : en
Publisher:
Release Date : 2019
Probabilistic Modeling For Novelty Detection With Applications To Fraud Identification written by Rémi Domingues and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Novelty detection is the unsupervised problem of identifying anomalies in test data which significantly differ from the training set. While numerous novelty detection methods were designed to model continuous numerical data, tackling datasets composed of mixed-type features, such as numerical and categorical data, or temporal datasets describing discrete event sequences is a challenging task. In addition to the supported data types, the key criteria for efficient novelty detection methods are the ability to accurately dissociate novelties from nominal samples, the interpretability, the scalability and the robustness to anomalies located in the training data. In this thesis, we investigate novel ways to tackle these issues. In particular, we propose (i) a survey of state-of-the-art novelty detection methods applied to mixed-type data, including extensive scalability, memory consumption and robustness tests (ii) a survey of state-of-the-art novelty detection methods suitable for sequence data (iii) a probabilistic nonparametric novelty detection method for mixed-type data based on Dirichlet process mixtures and exponential-family distributions and (iv) an autoencoder-based novelty detection model with encoder/decoder modelled as deep Gaussian processes. The learning of this last model is made tractable and scalable through the use of random feature approximations and stochastic variational inference. The method is suitable for large-scale novelty detection problems and data with mixed-type features. The experiments indicate that the proposed model achieves competitive results with state-of-the-art novelty detection methods.
Machine Learning For Disease Outbreak Detection Using Probabilistic Models
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Author : Nastaran Jafarpour Khameneh
language : en
Publisher:
Release Date : 2014
Machine Learning For Disease Outbreak Detection Using Probabilistic Models written by Nastaran Jafarpour Khameneh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
A Fast Probabilistic Method For Vehicle Detection And Tracking With An Explicit Contour Model
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Author : Wai-Sing Boris Yiu
language : en
Publisher:
Release Date : 2017-01-26
A Fast Probabilistic Method For Vehicle Detection And Tracking With An Explicit Contour Model written by Wai-Sing Boris Yiu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-26 with categories.
Probabilistic Models And Inference For Multi View People Detection In Overlapping Depth Images
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Author : Wetzel, Johannes
language : en
Publisher: KIT Scientific Publishing
Release Date : 2022-07-12
Probabilistic Models And Inference For Multi View People Detection In Overlapping Depth Images written by Wetzel, Johannes and has been published by KIT Scientific Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-12 with Technology & Engineering categories.
In this work, the task of wide-area indoor people detection in a network of depth sensors is examined. In particular, we investigate how the redundant and complementary multi-view information, including the temporal context, can be jointly leveraged to improve the detection performance. We recast the problem of multi-view people detection in overlapping depth images as an inverse problem and present a generative probabilistic framework to jointly exploit the temporal multi-view image evidence.
An Approach To Outlier Detection Based On Probabilistic Model And Map Criterion
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Author : Yariv Hasar
language : en
Publisher:
Release Date : 1999
An Approach To Outlier Detection Based On Probabilistic Model And Map Criterion written by Yariv Hasar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with categories.
A Fast Probabilistic Method For Vehicle Detection And Tracking With An Explicit Contour Model
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Author : Wai-sing Yiu (Boris)
language : en
Publisher:
Release Date : 2005
A Fast Probabilistic Method For Vehicle Detection And Tracking With An Explicit Contour Model written by Wai-sing Yiu (Boris) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Algorithms categories.
Robust Probabilistic Slow Feature Analysis For Soft Sensor Development And Model Quality Assessment
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Author : Cameron Dyson
language : en
Publisher:
Release Date : 2022
Robust Probabilistic Slow Feature Analysis For Soft Sensor Development And Model Quality Assessment written by Cameron Dyson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Machine learning categories.
Model predictive control (MPC) is a popular advanced control technology. Unfortunately, over time the behaviour of the plant may deviate from its initial design conditions resulting in model-plant-mismatch. The detection and diagnosis of such mismatches is an important task to ensure that MPC systems are operating optimally, and any potential model re-identification is targeted to only necessary sub-models. Conventional mismatch detection methods directly use plant operating data for such purposes. The quality of assessment of these methods may suffer in the presence of significant disturbances. In Chapter 2, a linear slow feature analysis (SFA) data reconstruction is proposed to remove fast and typically irrelevant variations, extracting only those slow-varying and important components of the data to detect model-plant-mismatches. This preprocessing approach is shown to improve the performance of a conventional model-plant-mismatch detection method through both simulated and industrial case studies, and thus provide a more targeted selection of sub-models for re-identification.As SFA does not directly model process noise, and the conventional probabilistic SFA (PSFA) extension treats all noises as Gaussian, these algorithms are susceptible to the presence of outliers in the data. As industrial data often contains outliers there is motivation to remedy this issue. In Chapter 3, a robust PSFA (rPSFA) method with the measurement noises modeled as a scale mixture of Gaussians, switched according to a Bernoulli distribution, is considered for the modelling of systems where data contains outliers. To demonstrate the effectiveness of the proposed method over regular SFA, conventional PSFA and a previously developed Student-t robust PSFA, simulations are conducted through Tennessee-Eastman benchmark process data. The algorithm is then applied to an industrial zinc roaster process.The developed rPSFA models the switching between inliers and outliers according to a Bernoulli distribution which is completely random with respect to the previous outlier-inlier state. Many industrial systems exhibit correlated noise behaviour in which an outlier is more likely to occur after another. To account for this, Chapter 4 replaces the Bernoulli distribution with a Hidden Markov Model (HMM) to allow for the previous measurement noise mode to influence the prediction of future noise modes. Further, current literature lacks an outlier robust PSFA based method that is designed to capture the behaviour of multi-modal systems. To this end, the proposed HMM based robust PSFA is implemented in a mixture model fashion, where multiple independent process models are developed simultaneously, and their results are blended according to some weightings. The proposed model is verified in a soft-sensor task for a simulated system with a single operating mode but with outliers generated according to a HMM. Additionally, an industrial system which contains outliers and displays two distinct operating modes is used to demonstrate the development of a soft-sensor and a MPC model-plant-mismatch detection and diagnosis task.
Probabilistic Modeling Of Process Systems With Application To Risk Assessment And Fault Detection
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Author : Taha Mohseni Ahooyi
language : en
Publisher:
Release Date : 2015
Probabilistic Modeling Of Process Systems With Application To Risk Assessment And Fault Detection written by Taha Mohseni Ahooyi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Chemical engineering categories.
Three new methods of joint probability estimation (modeling), a maximum-likelihood maximum-entropy method, a constrained maximum-entropy method, and a copula-based method called the rolling pin (RP) method, were developed. Compared to many existing probabilistic modeling methods such as Bayesian networks and copulas, the developed methods yield models that have better performance in terms of flexibility, interpretability and computational tractability. These methods can be used readily to model process systems and perform risk analysis and fault detection at steady state conditions, and can be coupled with appropriate mathematical tools to develop dynamic probabilistic models. Also, a method of performing probabilistic inference using RP-estimated joint probability distributions was introduced; this method is superior to Bayesian networks in several aspects. The RP method was also applied successfully to identify regression models that have high level of flexibility and are appealing in terms of computational costs.