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 20 of 81 for “"State-space Models"”.
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State-space models for stream networks
… throughout the stream network, we propose a state-space model to describe the spatial dependence in this tree-like structure with ordering based on flow. Developing a state-space formulation permits the use of the well known Kalman recursions. Variations of the Kalman Filter and Smoother are …
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Ensemble filtering for state space models
The state space model has been widely used in various fields including economics, finance, bioinformatics, oceanography, and tomography. The goal of the filtering problem is to find the posterior distribution of the hidden state given the current and past observations. The first part of my thesis …
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Latent state space models for prediction
… (Intensive Care Unit). I then use the generated models to predict blood pressure levels of ICU patients based on their historical ECG and ABP signals. The algorithm used is a variant of a Hidden Markov model. The new extension is termed as the Latent State Space Copula Model. In the novel Latent …
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State space models of remote manipulation tasks.
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1968.
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State space models for isolating neural oscillations
… We propose an alternative approach that uses state space models to represent basic physiological and dynamic principles, whose detailed structure and parameterization are informed by observed data. We find that this method can more accurately represent oscillatory power, effectively separating …
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Enhancing deep state space models for complex applications
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-08-01
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On maximum likelihood identification of state space models
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1979.
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Gaussian processes for state space models and change point detection
… time series problems. These are extended to the state space approach to time series in two different problems. We also combine Gaussian processes and Bayesian online change point detection (BOCPD) to increase the generality of the Gaussian process time series methods. These methodologies are …
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State-Space Models and Latent Processes in the Statistical Analysis of Neural Data
… we develop a more general approach to the state-space filtering problem. Our method solves the same recursive set of Markovian filter equations as the particle filter, but we replace all importance sampling steps with a more general Markov chain Monte Carlo (MCMC) step. Our algorithm is …
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Modelling Effective Connectivity in Functional Magnetic Resonance Imaging Data by State Space Models
This dissertation develops a new approach using state space models for effective connectivity analysis. The proposed approach integrates both activation and connectivity analysis into one single model. It takes the temporal information in the fMRI data into consideration explicitly and can be used …
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Methods for enhancing system dynamics modelling : state-space models, data-driven structural validation & discrete-event simulation
System dynamics (SD) simulation models are differential equation models that often contain a complex network of relationships between variables. These models are widely used, but have a number of limitations. SD models cannot represent individual entities, or model the stochastic behaviour of these …
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Sequential Modelling and Inference of High-frequency Limit Order Book with State-space Models and Monte Carlo Algorithms
… modelling approaches. By adopting powerful state-space models from the field of signal processing as well as a number of Bayesian inference algorithms such as particle filtering, Markov chain Monte Carlo and variational inference algorithms, this thesis presents my extensive research into …
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INVERSE APPROXIMATION THEORY OF RECURRENT MODELS FOR LEARNING SEQUENCES
… demonstrating the difficulty of recurrent models in learning long-term relationships, this dissertation presents a series of theoretical studies on the learning of long-term memories using recurrent models. First, based on the concept of a generalized memory function over nonlinear …
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Improved Underwater Vehicle Control and Maneuvering Analysis with Computational Fluid Dynamics Simulations
The quasi-steady state-space models generally used to simulate the dynamics of underwater vehicles perform well in most steady flow scenarios, and are therefore acceptable for modeling today\'s fleet of endurance-focused autonomous underwater vehicles (AUVs). However, with their usage of numerous …
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A comparison of advanced time series models for environmental dependent stock recruitment of the western rock lobster
Time series models have been applied in many areas including economics, stuck recruitment and the environment. Most environmental time series involve highly correlated dependent variables, which makes it difficult to apply conventional regression analysis, Traditionally, regression analysis has …
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Controlling Semiconductor Optical Amplifiers for Robust Integrated Photonic Signal Processing
… fibreline systems we derive three interrelated state-space models: a core photonic model, a photonic model with gain compression, and a equivalent circuit optoelectronic model. We validate each model and calibrate the gain compression model by pump/probe experiments. We then linearize the …
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Exponential Smoothing for Forecasting and Bayesian Validation of Computer Models
… We investigate three types of statistical models that have been found to underpin ES methods. They are ARIMA models, state space models with multiple sources of error (MSOE), and state space models with a single source of error (SSOE). We establish the relationship among the three classes …
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Ecosystem Models in a Bayesian State Space Framework
… being used to embed mechanistic process models used into statistical state space frameworks for environmental prediction and forecasting applications. In this study, I focus on Bayesian State Space Models (SSMs) for modeling the temporal dynamics of carbon in terrestrial ecosystems. In …
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Hidden state models for time series
… observations. In this thesis, we consider hidden space models for analysing and describing time series. We first provide an introduction to the principal concepts of hidden state models and draw an analogy between hidden Markov models and state space models. Central ideas such as hidden state …
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