Learning Summary Statistics for Approximate Bayesian Computation
نویسنده
چکیده
In high dimensional data, it is often very difficult to analytically evaluate the likelihood function, and thus hard to get a Bayesian posterior estimation. Approximate Bayesian Computation is an important algorithm in this application. However, to apply the algorithm, we need to compress the data into low dimensional summary statistics, which is typically hard to get in an analytical form. In this project, I used Radial Basis Function network and Neural Network to learn the summary statistics automatically. I constructed a time series example and a hidden markov chain example, and I find that Neural network performs better.
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