equadratures
v10 will be released early next year! Some of the planned features are listed below.
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Bayesian polynomial models
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Polynomial priors and posteriors for experimental vs. computational misfits
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Polynomial priors for multi-fidelity / multi-level models
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Polynomial coregional models for correlating similar polynomials
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Bayesian polynomial quadrature strategies
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Logistic regression in
equadratures
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Network polynomials
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A network lasso-type framework with simultaneous clustering and regression
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Sensitivity analysis for each cluster
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Subspace-based analysis
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Both $L_1$ and $L_2$ norm based cluster distance metrics
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A minimum-code
eqgo
class focused on end-to-end results -
Orthogonal polynomial expansions’ root finding for speeding up optimisations
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Adaptive quadrature rules for polynomial least squares
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Addition / subtraction / multiplication of polynomials
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Basic spline fitting functionality
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Enhanced syntax for streamlining the code
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Moving away from
param = Parameter(distribution='Normal', shape_parameter_A=2.5, shape_parameter_B=4.5, order=2)
to
param = Normal(mean=2.5, variance=4.5, order=2)
whilst maintaining backward compatibility. -
Having correlations defined as part of a parameter list class (that extends the default
list
class).
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