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Showing 1 to 20 of 23 for “"Approximate Bayesian computation"”.
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Approximate Bayesian Computation for Complex Dynamic Systems
… by real applications in biology, I propose computational strategies for Bayesian inference in contexts where standard Monte Carlo methods cannot be directly applied due to the high complexity of the dynamic model and/or data limitations.</p><p> Chapter 2 focuses on stochastic bionetwork …
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Summary statistics and sequential methods for approximate Bayesian computation
… models, but impossible to calculate likelihoods. Approximate Bayesian computation (ABC) is a method of inference for such models. It replaces calculation of the likelihood by a step which involves simulating artificial data for different parameter values, and comparing summary statistics of the …
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Calibrating high frequency trading data to agent based models using approximate Bayesian computation
We consider Sequential Monte Carlo Approximate Bayesian Computation (SMC ABC) as a method of calibration for the use of agent based models in market micro-structure. To date, there are no successful calibrations of agent based models to high frequency trading data. Here we test whether a more …
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Data conditioned simulation and inference
With the increasing power of personal computers, computational intensive statistical methods such as approximate Bayesian computation (ABC) are becoming an attractive and viable proposition to analyse complex statistical problems. There are three main aspects to ABC: • Proposing parameters. • …
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Calibrating a Latent Order Book Model to Market Data
… is calibrated using likelihood-free methods, Approximate Bayesian Computation (ABC) and an iterative extension, Population Monte-Carlo ABC (PMC-ABC) as well as a Black-box approach using the Nelder-Mead algorithm. We show that in the diffusion limit, the master equation becomes the LOB …
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New model-based methods for non-differentiable optimization
… parameter of the probabilistic model in a Bayesian manner, and thus provides a proper way to determine the diversity in the population of the models. We provide theoretical justification on the convergence of this framework by showing that the posterior distribution of the parameter …
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Understanding and managing Frogeye Leaf Spot through network-based modeling in soybean
… to improve FLS management in soybeans. Using Approximate Bayesian Computation, we estimated key epidemiological parameters and found that infection origin can shift the balance between transmission routes. Data analyses indicated that tillage and non-tillage plots did not differ significantly …
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Forest dynamics at regional scales: predictive models constrained with inventory data
… inventories combined with improvements in computational methods mean that models that incorporate the climate dependency of demographic processes may be parameterised at regional scales. In Chapter One I outline historical approaches to modelling forest dynamics and present a discussion of …
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Essays in Political Economics and Networks
… that our model can be estimated using a modified Approximate Bayesian computation method. We showcase our approach with three distinct empirical examples. The first example focuses on the legislative effectiveness of politicians in the 111th and 112th U.S. Congress. The second example looks at R&D …
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The demography of Red Sea reef fishes since the Last Glacial Maximum
… High-throughput sequencing data combined with an Approximate Bayesian Computation framework (including machine learning techniques) provided sufficient power to estimate population parameters for five reef fish species, Dascyllus abudafur, Dascyllus trimaculatus, Dascyllus marginatus, Pomacanthus …
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Scalable Estimation and Testing for Complex, High-Dimensional Data
… function. We introduce a wavelet-based approximate Bayesian computation approach that is likelihood-free and computationally scalable. This approach will be applied to two applications: estimating mutation rates of a generalized birth-death process based on fluctuation experimental data …
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Statistical algorithms using multisets and statistical inference of heterogeneous networks
Computational statistics, including methods such as Markov chain Monte Carlo (MCMC), bootstrap, approximate Bayesian computation, is an important part in modern statistics and has been widely used in many areas, such as Bayesian statistics, computational biology, and computational physics. In this …
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Modelling Past Land Routes in Archaeology: A ‘Roman Roads’ Theoretical and Methodological Rethinking
… of the Antonine Itinerary. Through the use of Approximate Bayesian Computation and Multi-Criteria Decision Analysis, it is shown that the influence of factors on the placement of Roman roads was not homogenous but rather reflects the specific circumstances that were present during their …
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Modelling and empirical approaches for predicting the invasiveness of alien species.
… Individual-based models (IBMs) were used, with approximate Bayesian computation (ABC) successfully applied in Chapter 2 to recreate an ongoing invasion and then predict the range expansion of the species. This model was then used in Chapter 3 to predicting the outcomes for the invasion of …
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Somatic evolution in healthy and chronically inflamed colon and skin
… fission. I apply a statistical framework called Approximate Bayesian Computation to estimate the crypt fission rate in the normal colon and in individuals with Familial adenomatous polyposis (FAP). I estimate the rate of crypt fission to be one every 27 years in the normal colon and one every 13 …
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Transport maps for accelerated Bayesian computation
Bayesian inference provides a probabilistic framework for combining prior knowledge with mathematical models and observational data. Characterizing a Bayesian posterior probability distribution can be a computationally challenging undertaking, however, particularly when evaluations of the posterior …
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On the advancement of optimal experimental design with applications to infectious diseases.
… the optimal experimental design within a Bayesian framework can be computationally inefficient, or infeasible. This is due to the need for many evaluations of the posterior distribution, and thus, the model likelihood - which is computationally intensive for most non-linear stochastic …
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Predicting the Spread and Management of the Cassava Brown Streak Disease Epidemic
… and immediately surrounding regions, we apply Approximate Bayesian Computation (ABC) to estimate dispersal parameters, providing methodological details on the development and validation of summary statistics. The model fitting also takes account of empirical data for vector density across …
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Spatial models of plant diversity and plant functional traits : towards a better understanding of plant community dynamics in fragmented landscapes
… model (IFM) with vegetation data using approximate Bayesian computation (ABC). I found that the type of regional plant community dynamics in the SJL is best characterized as a set of isolated “island communities” with very low connectivity between local communities. Model predictions …
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Systematics and Species Delimitation in New Guinea Skink Species Complexes (Squamata: Scincidae)
… Demographic analyses applied using approximate Bayesian computation and diffusion analysis further provide evidence for a complex demographic scenario in which migration between these populations continued for some time following their initial divergence, but subsequently decreased …
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