uravu.optimize

The optimize module includes the functionality necessary for maximum likelihood determination. Furthermore, the natural log likelihood function used in the mcmc() and nested_sampling() methods may be found here.

uravu.optimize.ln_likelihood(variables: numpy.ndarray, function: Callable, abscissa: numpy.ndarray, ordinate: numpy.ndarray) → float[source]

Calculate the natural logarithm of the likelihood given a set of variables, when there is no uncertainty in the abscissa.

Parameters:
  • variables – Variables for the function evaluation.
  • function – The function to be evaluated.
  • abscissa – The abscissa values.
  • ordinate – The ordinate values.
Returns:

Natural log-likelihood between model and data.
uravu.optimize.max_ln_likelihood(relationship: uravu.relationship.Relationship, method: str, x0: numpy.ndarray = None, **kwargs) → numpy.ndarray[source]

Determine the variable values which maximize the likelihood for the given relationship. For keyword arguments see the scipy.optimize.minimize() documentation.

Parameters:
  • relationship – The relationship for which variables should be found.
  • method – Method for optimisation to be used.
  • x0 – Initial guesses for the variable values. Default to the current variables values which are initialized as all 1.
Returns:

Optimized variables for the relationship.
uravu.optimize.negative_lnl(variables: numpy.ndarray, function: Callable, abscissa: numpy.ndarray, ordinate: numpy.ndarray) → float[source]

Calculate the negative natural logarithm of the likelihood given a set of variables, when there is no uncertainty in the abscissa.

Parameters:
  • variables – Variables for the function evaluation.
  • function – The function to be evaluated.
  • abscissa – The abscissa values.
  • ordinate – The ordinate values.
Returns:

Negative natural log-likelihood between model and data.