Essays about: "sequential Monte Carlo methods"
Showing result 1 - 5 of 13 essays containing the words sequential Monte Carlo methods.
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1. Longitudinal Group Sequential Testing
University essay from Uppsala universitet/Statistiska institutionenAbstract : Sequential testing is used in clinical trials and online experiments to terminate trials early if there is sufficient evidence of a treatment effect, reducing the subjects’ exposure to potentially harmful treatments. However, incomplete follow-up of trial subjects can lead to biased estimation of the treatment effect. READ MORE
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2. KL/TV Reshuffling : Statistical Distance Based Offspring Selection in SMC Methods
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Over the years sequential Monte Carlo (SMC), and, equivalently, particle filter (PF) theory has enjoyed much attention from researchers. However, the intensity of developing innovative resampling methods, also known as offspring selection methods, has long been declining, with most of the popular schemes aging back two decades. READ MORE
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3. Applying Model Selection on Ligand-Target Binding Kinetic Analysis
University essay from KTH/ProteinvetenskapAbstract : The time-course of interaction formation or breaking can be studied using LigandTracer, and the data obtained from an experiment can be analyzed using a model of ligand-target binding kinetics. There are different kinetic models, and the choice of model is currently motivated by knowledge about the interaction, which is problematic when the knowledge about the interaction is unsatisfactory. READ MORE
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4. Particle Filter Bridge Interpolation in GANs
University essay from KTH/Matematisk statistikAbstract : Generative adversarial networks (GANs), a type of generative modeling framework, has received much attention in the past few years since they were discovered for their capacity to recover complex high-dimensional data distributions. These provide a compressed representation of the data where all but the essential features of a sample is extracted, subsequently inducing a similarity measure on the space of data. READ MORE
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5. Particle-Based Online Bayesian Learning of Static Parameters with Application to Mixture Models
University essay from KTH/Matematisk statistikAbstract : This thesis investigates the possibility of using Sequential Monte Carlo methods (SMC) to create an online algorithm to infer properties from a dataset, such as unknown model parameters. Statistical inference from data streams tends to be difficult, and this is particularly the case for parametric models, which will be the focus of this paper. READ MORE