Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 5 of 5 for “"State space modelling"”.
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State space modelling of extreme values with particle filters
State space models are a flexible class of Bayesian model that can be used to smoothly capture non-stationarity. Observations are assumed independent given a latent state process so that their distribution can change gradually over time. Sequential Monte Carlo methods known as particle filters …
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Constructing an Informative Prior Distribution of Noises in Seasonal Adjustment
… models and linear lters. On the other hand, state space modelling (abbreviated to SSM) is also a popular method to solve this problem and researchers including J. Durbin, S.J. Koopman and and A. Harvery have contributed a lot of work to it. Unlike linear lters and ARIMA models, the study on …
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Methods for enhancing system dynamics modelling : state-space models, data-driven structural validation & discrete-event simulation
… limitations and hence enhance system dynamics modelling. This research is undertaken in the context of SD models from a major telecommunications provider. In the first part of the thesis we investigate the advantages of adding a discreteevent simulation model to an existing SD model, to form a …
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The role of the BHLH038 transcription factor in the regulation of osmotic and drought stress responses in Arabidopsis thaliana
… Regulatory Networks using Variational Bayesian State Space Modelling, obtained from time-series slow drying microarray data. These Gene Regulatory Networks unveiled various Transcription Factors such as BHLH038 closely related to AGL22 a key hub gene for drought response in Arabidopsis as …
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Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models
… literature, greatly extends them and allows new state-of-the-art approximations to emerge. We start by building a posterior approximation framework based on Power-Expectation Propagation for Gaussian process regression and classification. This framework relies on a structured approximate Gaussian …