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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 21 for “"Nested sampling"”.
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Bayesian Methods and Machine Learning in Astrophysics
… discuss two related themes: advances in nested sampling (Chapters 3 to 5), and Bayesian sparse reconstruction of signals from noisy data (Chapters 6 and 7). Nested sampling is a popular method for Bayesian computation which is widely used in astrophysics. Following the introduction and …
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FireFly: A Bayesian Approach to Source Finding in Astronomical Data
… information. In this thesis, we implement nested sampling and Monte Carlo Markov Chain (MCMC) techniques to develop a new Bayesian source finding technique called FireFly. FireFly employs a technique of switching ‘on’ and ‘off’ sources during sampling to deal with the fact that we don’t …
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Bayesian modeling of microwave foregrounds
… on current cosmological models. The method of nested sampling [16, 5], a Bayesian inference technique for calculating the evidence (the average of the likelihood over the prior mass), promises to be efficient and accurate for modeling the microwave foregrounds masking the CMB signal. An …
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Fitting Interatomic Potentials to Reproduce Phase Transitions
… To calculate phase transitions, I use the nested sampling algorithm which was already established in the literature. The Ti nested sampling results are verified with thermodynamic integration. My Nelder-Mead implementation allows for semi-automatic submission and analysis of the nested …
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Simulation-based Bayesian machine learning methods for Cosmology and beyond
… PolySwyft. This sequential simulation- based nested sampler is motivated by the limitations of likelihood-based Bayesian inference in sky-averaged 21-cm Cosmology. Moreover, PolySwyft merges nested sampling and neural ratio estimation into a general Bayesian framework, and the method is a …
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Mathematical modelling of the floral transition — with a Bayesian flourish —
… of a contemporary Bayesian inference algorithm, nested sampling, for inference problems typically found in systems biology where the data are few and noisy. Nested sampling simultaneously calculates the key term for model comparison and also produces parameter inferences allowing uncertainty in …
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Physical modelling of galaxy clusters and Bayesian inference in astrophysics
… a new Bayesian inference algorithm based on nested sampling is presented. The algorithm, named the "geometric nested sampler", is an adaption of the Metropolis-Hastings nested sampler and makes use of the geometrical interpretation of sets of parameters to sample from their domains …
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Detecting episodes of star formation using Bayesian model selection.
… and Bayes factors for multiple scenarios of nested models. In addition, we investigate the role that prior specification has in the derivation of physical parameters. These results are then compared to Bayes factors calculated using the Savage-Dickey Density Ratio (SDDR). The results of this …
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Timescales of magma residence and transport underneath Iceland
… (Diffusion chronometry using Finite Elements and Nested Sampling). This method combines a flexible finite element numerical model with a nested sampling Bayesian inversion to provide robust uncertainty estimates and account for observations from multiple elements within a single phase, or multiple …
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Data-driven linear interatomic potentials
… is presented. This workflow involves driving nested sampling simulations using previously mentioned data-driven interatomic potentials to approximate the partition function with first-principles accuracy. From the partition function, free energies and specific heat capacities are then …
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Scalable Full Posterior Inference for Uncertainty-Aware Robot Perception
… solutions to full posterior inference via nested sampling. Additionally, we develop a streaming platform that connects mobile devices and servers through web applications to conduct live demos of object-based SLAM, featuring the sharing of mapping results among online peers and continuous …
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Activities Within a Myotine Bat Community With Emphasis on the Endangered Indiana Bat, Myotis Sodalis
… calls using Anabat II bat detectors. A nested sampling design was used with five habitat types which include upland and lowland sites within each habitat. Calls were identified to species using a linear discriminate function analysis and a library of known calls. The three species of …
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Computational Bayesian techniques applied to cosmology
… advance in Bayesian model selection using nested sampling, as the method is completely general and straightforward to implement. We note that efficiency gains are not guaranteed and may be problem specific: further research is needed.
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Sampling Configurational Energy Landscapes
… which is a key limiting step in discrete path sampling. The efficiency of the transition state search is strongly dependent on the quality of the initial interpolation and so the alignment methods used. In this work two novel alignment algorithms are presented and benchmarked against existing …
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Experimental data analysis techniques for validation of Tokamak impurity transport simulations
… previous approaches through use of multimodal nested sampling is developed and benchmarked using synthetic data. These tests reveal that uncertainties in the transport coefficient profiles previously attributed to uncertainties in the temperature and density profiles are in fact entirely …
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Techniques and Technologies for Earth-twin Discoveries
… poorly sampled. The analysis is conducted in a nested-sampling Bayesian framework and thus allows for the direct statistical comparison of different planetary models given some data set, and produces full posterior estimation for all the parameters of all the models. I used this analysis …
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Bayesian methods for gravitational waves and neural networks
… network training algorithm is combined with the nested sampling algorithm MULTINEST to provide rapid Bayesian inference. Using samples from the normal inference, a network is trained on the likelihood function and eventually used in its place. This is able to provide significant increase in the …
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Invariant polynomials and machine learning
… from a Bayesian inference perspective and employ nested sampling techniques to perform model comparison. Beyond a certain network size, we find that networks utilising Hironaka decompositions perform the best.
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Diversity, Invasibility, and Stability of Appalachian Forests across an Experimental Disturbance Gradient
… of spatial scales (2 hectares to 1 m2) using a nested sampling design and was also sampled at three times including pre-disturbance, one year post-disturbance, and ten year post-disturbance. For one element of the study I tested modern theories of biological invasions and investigated how the …
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The Bayesian Global Sky Model (B-GSM)
… and calibration into the model. We use nested sampling to compute Bayesian evidence and to determine posterior distributions for the spectral behaviour and spatial amplitudes of diffuse emission components. Bayesian model comparison, using these Bayesian evidence values, is then used to …
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