Density Estimation For Geospatial Imagery Using Autoregressive Models
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Bayesian machine learning is all about learning a good representation of very complicated datasets, leveraging cleverly structured models and effective parameter estimation techniques to create a high-dimensional probability distribution approximating the observed data. A key advantage of posing computer vision research under the umbrella of Bayesian inference is that some tasks become really straightforward with the right choice of model.