mcmc_target Derived Type

type, public, abstract :: mcmc_target

Defines a model of the target distribution(s).


Components

Type Visibility Attributes Name Initial
real(kind=real64), public :: data_noise = 1.0d0

A parameter representing the noise in the data.

Type-Bound Procedures

procedure, public :: add_parameter => mt_add_param

  • private subroutine mt_add_param(this, x)

    Adds a new model parameter.

    Arguments

    Type IntentOptional Attributes Name
    class(mcmc_target), intent(inout) :: this

    The mcmc_target object.
    class(distribution), intent(in) :: x

    The parameter to add.

procedure, public :: evaluate_prior => mt_eval_prior

  • private function mt_eval_prior(this, x) result(rst)

    Evaluates the PDF's for each parameter and computes a probability.

    Arguments

    Type IntentOptional Attributes Name
    class(mcmc_target), intent(in) :: this

    The mcmc_target object.
    real(kind=real64), intent(in), dimension(:) :: x

    An N-element array containing the values at which to evaluate each of the N parameter PDF's.

    Return Value real(kind=real64)

    The resulting probability.

procedure, public :: evaluate_variance_prior => mt_eval_var_prior

  • private pure function mt_eval_var_prior(this, x) result(rst)

    Evalautes the model variance prior PDF.

    Arguments

    Type IntentOptional Attributes Name
    class(mcmc_target), intent(in) :: this

    The mcmc_target object.
    real(kind=real64), intent(in) :: x

    The value at which to evaluate the variance prior distribution PDF.

    Return Value real(kind=real64)

    The value of the variance prior distribution's PDF.

procedure, public :: get_parameter => mt_get_param

  • private function mt_get_param(this, i) result(rst)

    Gets a pointer to the stored parameter.

    Arguments

    Type IntentOptional Attributes Name
    class(mcmc_target), intent(in) :: this

    The mcmc_target object.
    integer(kind=int32), intent(in) :: i

    The index of the parameter to retrieve. If outside the bounds of the collection of parameters a null pointer is returned.

    Return Value class(distribution), pointer

    A pointer to the requested parameter distribution.

procedure, public :: get_parameter_count => mt_get_param_count

  • private pure function mt_get_param_count(this) result(rst)

    Gets the number of model parameters.

    Arguments

    Type IntentOptional Attributes Name
    class(mcmc_target), intent(in) :: this

    The mcmc_target object.

    Return Value integer(kind=int32)

    The parameter count.

procedure, public :: likelihood => mt_likelihood

  • private function mt_likelihood(this, xdata, ydata, xc, var) result(rst)

    Computes the target likelihood.

    Arguments

    Type IntentOptional Attributes Name
    class(mcmc_target), intent(inout) :: this

    The mcmc_target object.
    real(kind=real64), intent(in), dimension(:) :: xdata

    An M-element array containing the independent data points.
    real(kind=real64), intent(in), dimension(:) :: ydata

    An M-element array containing the dependent data points.
    real(kind=real64), intent(in), dimension(:) :: xc

    An N-element array containing the model parameters.
    real(kind=real64), intent(in) :: var

    An estimate of the model variance.

    Return Value real(kind=real64)

    The likelihood value.

procedure(evaluate_model), public, deferred :: model

  • subroutine evaluate_model(this, xdata, xc, y) Prototype

    Evaluates the model at the supplied values.

    Arguments

    Type IntentOptional Attributes Name
    class(mcmc_target), intent(in) :: this

    The mcmc_target object.
    real(kind=real64), intent(in), dimension(:) :: xdata

    An M-element array containing the values at which to evaluate the model.
    real(kind=real64), intent(in), dimension(:) :: xc

    An N-element array containing the model parameters.
    real(kind=real64), intent(out), dimension(:) :: y

    An M-element array where the resulting model values wil be written.

procedure, public :: sample_variance_prior => mt_sample_var_prior

  • private function mt_sample_var_prior(this, vc, n) result(rst)

    Samples the variance prior distribution for the requested number of samples.

    Arguments

    Type IntentOptional Attributes Name
    class(mcmc_target), intent(inout) :: this

    The mcmc_target object.
    real(kind=real64), intent(in) :: vc

    The prior variance term.
    integer(kind=int32), intent(in) :: n

    The number of samples.

    Return Value real(kind=real64), allocatable, dimension(:)

    The requested samples.