Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification

Details Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification

1997-01-28

arxiv.org/abs/physics/9701026v2

Physics

Data Analysis, Statistics and Probability

Scientific paper

Gaussian processes are a natural way of defining prior distributions over functions of one or more input variables. In a simple nonparametric regression problem, where such a function gives the mean of a Gaussian distribution for an observed response, a Gaussian process model can easily be implemented using matrix computations that are feasible for datasets of up to about a thousand cases. Hyperparameters that define the covariance function of the Gaussian process can be sampled using Markov chain methods. Regression models where the noise has a t distribution and logistic or probit models for classification applications can be implemented by sampling as well for latent values underlying the observations. Software is now available that implements these methods using covariance functions with hierarchical parameterizations. Models defined in this way can discover high-level properties of the data, such as which inputs are relevant to predicting the response.

Affiliated with

Also associated with

No associations

Rating

Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-672784