Global ETD Search
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Showing 1 to 11 of 11 for “"approximate Bayesian computation (ABC)"”.
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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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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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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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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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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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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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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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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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Modelling animal movement in heterogeneous environments: from statistical inferential models to individual-based models
… of unobserved behaviours. Approaches based on Approximate Bayesian computation (ABC) methods have been used to support the parameterisation, calibration and evaluation of IBMs. However, the ABC approach requires selection and use of data to exclude parameter sets and unrealistic model …
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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 …