mrpy.extra.reparameterise¶
Provides classes which implement variations of the MRP, in which the parameters have been transformed.
Each transformation has three available classes:
- one with a suffix MRP, which implements the core MRP quantities (i.e. is a subclass of
mrpy.core.MRP)- one with a suffix PerObj, which extends the previous one for likelihoods based on samples of variates (i.e. is a subclass of
mrpy.likelihoods.PerObjLike)- one with a suffix Curve, which extends the base one for likelihoods based on chi-squared minimzation against binned data (i.e. a subclass of
mrpy.likelihoods.CurveLike)
In all, the transformed parameters are denoted p1,p2,p3.
In addition, base classes for each are provided, which makes it easy to implement
arbitrary transformations. See the docs for ReparameteriseMRP for more
details.
Classes
AP1Curve([p1, p2, p3, logHs, alpha, beta]) |
|
AP1Sample([p1, p2, p3, logHs, alpha, beta]) |
|
Ap1MRP([p1, p2, p3, logHs, alpha, beta]) |
A fairly standard parameterisation of the TGGD (eg. |
GG2Curve([p1, p2, p3, logHs, alpha, beta]) |
|
GG2MRP([p1, p2, p3, logHs, alpha, beta]) |
A reparameterisation of the standard MRP form of the TGGD (eg. |
GG2Sample([p1, p2, p3, logHs, alpha, beta]) |
|
GG3Curve([p1, p2, p3, logHs, alpha, beta]) |
|
GG3MRP([p1, p2, p3, logHs, alpha, beta]) |
A reparameterisation of the standard MRP form of the TGGD (eg. |
GG3Sample([p1, p2, p3, logHs, alpha, beta]) |
|
HTCurve([p1, p2, p3, logHs, alpha, beta]) |
|
HTMRP([p1, p2, p3, logHs, alpha, beta]) |
A reparameterisation of the standard MRP form of the TGGD. |
HTSample([p1, p2, p3, logHs, alpha, beta]) |
|
ReparameteriseCurveLike([p1, p2, p3, logHs, …]) |
An extension of ReparameteriseMRP which adds necessary methods for calculating jacobians and hessians for chi-square likelihoods. |
ReparameteriseMRP([p1, p2, p3, logHs, …]) |
Base class for reparameterising the MRP. |
ReparameteriseSampleLike([p1, p2, p3, …]) |
An extension of ReparameteriseMRP which adds necessary methods for calculating jacobians and hessians for per-object likelihoods. |