bayesbay.perturbations.ParamPerturbation

class bayesbay.perturbations.ParamPerturbation(param_space_name, parameters)

Perturbation on a chosen parameter value

Parameters:

• param_name (str) – the name of the parameter to be perturbed

• parameters (List[Prior]) – list containing the Prior instances to be perturbed

Reference Details

type

type of current perturbation (i.e. class name)

Returns:

the name of this current class

Return type:

str

perturb(state)

perturb one value for each parameter in self.parameters, returning a proposed state and the log of the corresponding partial acceptance probability

\[\begin{split}\underbrace{\alpha_{p}}_{\begin{array}{c} \text{Partial} \\ \text{acceptance} \\ \text{probability} \end{array}} = \underbrace{\frac{p\left({\bf m'}\right)}{p\left({\bf m}\right)}}_{\text{Prior ratio}} \underbrace{\frac{q\left({\bf m} \mid {\bf m'}\right)}{q\left({\bf m'} \mid {\bf m}\right)}}_{\text{Proposal ratio}} \underbrace{\lvert \mathbf{J} \rvert}_{\begin{array}{c} \text{Jacobian} \\ \text{determinant} \end{array}},\end{split}\]

Parameters:

state (State) – the current state to perturb from

Returns:

proposed new state and \(\alpha_{p} = \log( \frac{p({\bf m'})}{p({\bf m})} \frac{q\left({\bf m} \mid {\bf m'}\right)}{q\left({\bf m'} \mid {\bf m}\right)} \lvert \mathbf{J} \rvert)\)

Return type:

Tuple[State, Number]

perturb_param_space_state(ps_state)

proposes a new parameter space state from the given state and calculates the log of the corresponding partial acceptance probability

Parameters:

state (ParameterSpaceState) – the given current parameter space state

Returns:

the proposed new parameter space state and the partial acceptance probability

Return type:

Tuple[State, Number]

__call__(state)

same as perturb()

Parameters:

state (State) – the given current state

Returns:

proposed new state and the partial acceptance probability excluding log likelihood ratio for this perturbation

Return type:

Tuple[State, Number]

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