Global ETD Search

211	Bayesian Non-parametric Models for Time Series Decomposition Granados-Garcia, Guilllermo 05 January 2023 (has links) The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify frequency bands, defined apriori, that have the most substantial relative contributions to the overall variance of the signal. However, a limitation of this approach is that the precise frequency and bandwidth of oscillations are not uniform across cognitive demands. Thus, these bands should not be arbitrarily set in any analysis. To overcome this limitation, we propose three Bayesian Non-parametric models for time series decomposition which are data-driven approaches that identifies (i) the number of prominent spectral peaks, (ii) the frequency peak locations, and (iii) their corresponding bandwidths (or spread of power around the peaks). The standardized SDF is represented as a Dirichlet process mixture based on a kernel derived from second-order auto-regressive processes which completely characterize the location (peak) and scale (bandwidth) parameters. A Metropolis-Hastings within Gibbs algorithm is developed for sampling from the posterior distribution of the mixture parameters for each project. Simulation studies demonstrate the robustness and performance of the proposed methods. The methods developed were applied to analyze local field potential (LFP) activity from the hippocampus of laboratory rats across different conditions in a non-spatial sequence memory experiment to identify the most prominent frequency bands and examine the link between specific patterns of brain oscillatory activity and trial-specific cognitive demands. The second application study 61 EEG channels from two subjects performing a visual recognition task to discover frequency-specific oscillations present across brain zones. The third application extends the model to characterize the data coming from 10 alcoholics and 10 controls across three experimental conditions across 30 trials. The proposed models generate a framework to condense the oscillatory behavior of populations across different tasks isolating the target fundamental components allowing the practitioner different perspectives of analysis. Time Series Markov Chain Monte Carlo Mixture Models Bayesian Non-parametrics EEG Analysis Waveform decomposition
212	Bayesian Restricted Likelihood Methods Lewis, John Robert January 2014 (has links) No description available. Statistics restricted likelihood Markov chain Monte Carlo M-estimation robust regression
213	Optimization of Financial Decision for Elder Care Services Using Markov Chain Modeling Dai, Honghao 15 June 2017 (has links) No description available. Engineering Markov Chain Long-term care Optimization Medicare Long-term care insurance Reverse Mortgage
214	Application of Passive and Active Microwave Remote Sensing for Snow WaterEquivalent Estimation Pan, Jinmei 26 October 2017 (has links) No description available. Earth Geography Hydrologic Sciences
215	Function Registration from a Bayesian Perspective Lu, Yi January 2017 (has links) No description available. Statistics Function registration time warping Markov chain Monte Carlo Bayesian method
216	TWO ESSAYS IN BAYESIAN PENALIZED SPLINES LI, MIN 16 September 2002 (has links) No description available. Statistics smoothing spline reversible jump Markov Chain Monte Carlo credit spreads forward rates
217	The Discrete Threshold Regression Model Stettler, John January 2015 (has links) No description available. Statistics Threshold regression Markov chain time-varying covariates discrete-state discrete-time
218	Image Parsing by Data-Driven Markov Chain Monte Carlo Tu, Zhuowen 20 December 2002 (has links) No description available. Computer Science Image Segmentation Image Parsing Region Curve Markov Chain Monte Carlo
219	Bayesian inference on dynamics of individual and population hepatotoxicity via state space models Li, Qianqiu 24 August 2005 (has links) No description available. Statistics Hepatotoxicity Mean Reversion State Space Stochastic Inference Drug Concentration Markov Chain Monte Carlo Empirical Bayesian
220	Multiple imputation for marginal and mixed models in longitudinal data with informative missingness Deng, Wei 07 October 2005 (has links) No description available. Generalized estimating equations non-ignorable missingness dropout Markov chain Monte Carlo (MCMC) Gibbs' sampler

Search results