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Skip to Search Results- 18Particle filters
- 2Bayesian Model Selection
- 2Branching Process
- 2Central Limit Theorem
- 2Ensemble Kalman filter
- 2Law of large numbers
- 7Kouritzin, Michael
- 2Wu, Biao
- 1Balakrishnapillai Chitralekha, Saneej
- 1Blount, Douglas
- 1Deng,Jing
- 1Hajizadeh, Mohammad
- 10Graduate and Postdoctoral Studies (GPS), Faculty of
- 10Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 8Mathematical and Statistical Sciences, Department of
- 8Mathematical and Statistical Sciences, Department of/Research Publications (Mathematical and Statistical Sciences)
- 2Huang, Biao (Chemical and Materials Engineering)
- 1Bulitko, Vadim (Computing Science)
- 1Dr. Shah, Sirish (Chemical and Materials Engineering)
- 1Huang, Biao (Department of Chemical and Materials Engineering)
- 1Kouritzin, Mike (Mathematical and Statistical Sciences)
- 1Lipsett, Michael (Mechanical Engineering)
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2003
Kouritzin, Michael, Ma, Xinjian, Long, Hongwei, Sun, Wei
Nonlinear filtering is an important and effective tool for handling estimation of signals when observations are incomplete, distorted, and corrupted. Quite often in real world applications, the signals to be estimated contain unknown parameters which need to be determined. Herein, we develop and...
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Particle Filter for Bayesian State Estimation and Its Application to Soft Sensor Development
DownloadSpring 2012
For chemical engineering processes, state estimation plays a key role in various applications such as process monitoring, fault detection, process optimization and model based control. Thanks to their distinct advantages of inference mechanism, Bayesian state estimators have been extensively...
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Fall 2009
Commercial video game developers constantly strive to create intelligent humanoid characters that are controlled by computers. To ensure computer opponents are challenging to human players, these characters are often allowed to cheat. Although they appear skillful at playing video games,...
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2015-04-20
A large class of discrete-time branching particle filters with Bayesian model selection ca-pabilities and effective resampling is introduced in algorithmic form, shown empirically to outperform the popular bootstrap algorithm and analyzed mathematically. The particles interact weakly in the...
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RESERVOIR HISTORY MATCHING USING CONSTRAINED ENSEMBLE KALMAN FILTER AND PARTICLE FILTER METHODS
DownloadSpring 2015
The high heterogeneity of petroleum reservoirs, represented by their spatially varying rock properties (porosity and permeability), greatly dictates the quantity of recoverable oil. In this work, the estimation of these rock properties, which is crucial for the future performance prediction of a...
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Fall 2010
Estimation theory is a branch of statistics and probability that derives information about random variables based on known information. In process engineering, state estimation is used for a variety of purposes, such as: soft sensing, digital filter design, model predictive control and...
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Spring 2016
In this thesis, we first introduce two basic problems of filter, the nonlinear filtering and model selection problem. We show that both of them can be solved by the unnormalized filter approach. Then several web based particle filter algorithms will be discussed. We extend the resampled and...