Development plan

General structure

The majority of code is to be within Python modules and structured by using classes corresponding to the main concepts :

• density estimator
• optimization via cross-validation
• uncertainty estimation via bootstrap iteration
• reweighting of input measurement samples

Density estimator

Most basic element, can give a density estimate at any point in physical parameter space.

Information required: Physical parameter values, ie observations (n-dimensional), bandwidths (n-dimensional), adaptive parameter ('alpha')

• The bandwidths should either be fixed (1 bw per dimension) or general/adaptive, ie 1 bw per dimension for each observation

Auxiliary information: Parameter names, log parameter flags (is the parameter the log of a physical quantity), units

Initialization: Train base density estimator with the given parameter points and bandwidths/alpha

Attributes: Aux information, parameter values, bandwidths/alpha, base estimator

Method: Evaluate density at any parameter point or array of points

Method: Set bandwidths/alpha & retrain

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