Bayesian parameter estimation using amortized variational inference.
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Date
2020
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Publisher
Universidad de Concepción
Abstract
Through the use of models, it is possible to express information about a process or
data being analyzed. These models seek to explain or predict the process of interest.
Models with a known and fixed number of parameters, known as parametric models,
let us interpret more easily the phenomena being studied by incorporating assumptions
directly in the modeling part through the parameters. The parameters of the model must
be estimated to accomplish a specific task related to the data being used. One way of
finding these parameters is by using the Bayesian approach, which provides information
about the uncertainty of the model being used as well as including prior information into
the learning process. Methods commonly used for Bayesian inference, such as Markov
Chain Monte Carlo or the faster but approximate Variational Inference methods are not
suited when the model is used multiple times on different sets of data. This is because they
work on one set of observations at a time, which means that the same procedure must be
repeated from scratch to find the parameters of the model that work best for the new data.
Instead, we provide experiments on synthetic data that show how Amortized Variational
Inference can be used to obtain results comparable to more precise methods while reducing
the inference time for new observations by making use of global information learned via
an inference network. Our results show that our approximate inference procedure can
provide results similar to classic methods even in the presence of noise for simple models.
Description
Tesis presentada para optar al título de Ingeniero Civil Informático.