Learning of Milky Way Model Parameters Using Matrixvariate Data in a New Gaussian Processbased Method
Add to your list(s)
Download to your calendar using vCal
If you have a question about this talk, please contact Zoubin Ghahramani.
In this talk I will discuss a new Bayesian nonparametric method of predicting the value of the model parameter vector that supports real observed data, where this measured information is in the form of a matrix. The information is then expressed as an unknown, matrixvariate function of the model parameter vector and this unknown function is modelled using a highdimensional Gaussian Process. The model is trained on a training data set that is generated (via simulations) at a chosen design set. In fact, in our treatment of the information as a vector of corresponding dimensions, this function is modelled as a vectorvariate Gaussian Process leading to the likelihood being matrixnormal in nature, with mean and covariance matrices suggested by the structure of the Gaussian Process in question. In an effort to learn selected process parameters (such as the smoothness parameters) from the data, in addition to the unknown model parameter vector value that supports the real data, we write their joint posterior probability, given training as well as observed data. Inference is performed using Transformationbased MCMC . An application of this method is made to learn feature parameters of the Milky Way, using measured and simulated data of velocity vectors of stars that live in the vicinity of the Sun. Learning of the Galactic parameters with the real data is shown to produce a similar result to a comparator method that requires a much larger data set, in order to accomplish estimation.
This talk is part of the Machine Learning @ CUED series.
This talk is included in these lists:
Note that exdirectory lists are not shown.
