Design for Gaussian Process

[sourish-cmi]

Initiating the discussion for Gaussian Process regression

[sourish-cmi]

I am thinking about the design for GP Regression could be performed something like this:

container = fit(Formula, data::DataFrame, modelClass::GaussianProcessRegression,MeanFunction::MeanZero,kernelClass::Exponential)

For example,

container = fit(y~x1+x2+x3,train_data,GaussianProcessRegression(),MeanZero(),Exponential())

• This could implement the default as MLE through Optimize using the GaussianProcess.jl.

The Bayesian method can be implemented using the following way:

container = fit(meanFun::Formula, kernelFun::Formula, data::DataFrame, modelClass::GaussianProcessRegression,kernelClass::Exponential
,prior::Prior_Normal(),sim_size::Int64)

For example,

container = fit(y~x1+x2+x3,train_data,GaussianProcessRegression(),Exponential(),Prior_Cauchy(),10000)

• This could be implemented using the HMC or MCMC using the Turing.jl.

@ajaynshah @ayushpatnaikgit @codetalker7 @ShouvikGhosh2048