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Showing 1 to 11 of 11 for “"maximum likelihood parameter estimation"”.
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Maximum likelihood parameter estimation in time series models using sequential Monte Carlo
… typically contains a static variable, called parameter, which parametrizes the joint law of the random variables involved in the definition of the model. When a time series model is to be fitted to some sequentially observed data, it is essential to decide on the value of the parameter that …
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An object-oriented, maximum-likelihood parameter estimation program for GARCH(p,q)
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
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Maximum likelihood parameter estimation of mixture models and its application to image segmentation and restoration
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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A Dynamic Model of Nuclear Energy Market Share Employing Full Information Maximum Likelihood Parameter estimation and Extended Kalman Filtering
Made available in DSpace on 2017-07-06T18:42:20Z (GMT). No. of bitstreams: 3 Arthur_William_B_MS.pdf: 73243182 bytes, checksum: ca6a2f2b7245fe7bbb954caa60e6f0b5 (MD5) Arthur_William_B_MS_ABS.pdf: 1097901 bytes, checksum: 74a8e84ddcd0435d126fa4385f60c5ef (MD5) license.txt: 4813 bytes, checksum: …
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Ill-conditioned information matrices and the generalized linear model: an asymptotically biased estimation approach
… model (Nelder and Wedderburn (1972)), interative maximum likelihood parameter estimation is employed via the method of scoring. This iterative procedure involves a key matrix, the information matrix. Ill-conditioning of the information matrix can be responsible for making many desirable properties …
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Robust Speech Filter And Voice Encoder Parameter Estimation using the Phase-Phase Correlator
… performance has been achieved via the use of a maximum likelihood parameter estimation of an auto-regressive model of order ten that best fits the speech signal under the assumption that the signal and the noise are Gaussian stochastic processes. However, this method breaks down in the presence …
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Multiscale Systems Theory With Application to Electrodeposition and Crystallization Process
… A methodology that includes stochastic parameter sensitivity analysis, maximum likelihood parameter estimation, and experimental design was developed for multiscale systems, to obtain a reliable model that is sufficiently accurate for use in robust optimal design and control studies. The …
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Parametric estimation of superimposed signals
The problem of parametric estimation of signals composed of a weighted sum of functions drawn from a known parametric family with unknown parameters in white Gaussian noise was studied. New closed-form expressions of the Cramer-Rao bound (CRB) for parametric estimation of superimposed signals in …
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Generative modeling of sequential data
… (modeling choices), learning paradigm (e.g. maximum likelihood, method of moments, adversarial training), and optimization. For the representation aspect, we make the following contributions: -We argue that using a multi-modal latent representation (unlike popular methods such as variational …
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Parameter Estimation Techniques for Nonlinear Dynamic Models with Limited Data, Process Disturbances and Modeling Errors
… overcome two types of problems that occur during parameter estimation in chemical engineering systems are studied. The first problem is having too many parameters to estimate from limited available data, assuming that the model structure is correct, while the second problem involves estimating …