/usr/include/JAGS/distribution/ScalarDist.h is in jags 4.2.0-2.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | #ifndef SCALAR_DIST_H_
#define SCALAR_DIST_H_
#include <distribution/Distribution.h>
namespace jags {
struct RNG;
/**
* Enumerates three possible ranges of support for a scalar random
* variable.
*
* DIST_UNBOUNDED for support on the whole real line
*
* DIST_POSITIVE for support on values > 0
*
* DIST_PROPORTION for support on values in [0,1]
*
* DIST_SPECIAL for other distributions, e.g. distributions where the
* support depends on the parameter values.
*/
enum Support {DIST_UNBOUNDED, DIST_POSITIVE, DIST_PROPORTION, DIST_SPECIAL};
/**
* @short Scalar distributions
*
* The ScalarDist class represents distributions that are scalar-valued
* and for which the parameters are also scalars.
*/
class ScalarDist : public Distribution
{
const Support _support;
public:
/**
* Constructor
*
* @param name BUGS language name of distribution
*
* @param npar Number of parameters, excluding upper and lower bound
*
* @param support Support of distribution
*
* @param discrete Logical flag indicating whether the distribution
* is discrete-valued.
*/
ScalarDist(std::string const &name, unsigned int npar, Support support);
/**
* This implementation should be used for distributions with
* Support DIST_UNBOUNDED, DIST_POSITIVE and DIST_PROPORTION. If
* the Support is DIST_SPECIAL, this must be overloaded.
*/
bool isSupportFixed(std::vector<bool> const &fixmask) const;
/**
* Lower limit of distribution, given parameters. If the
* distribution has no lower limit, this should return JAGS_NEGINF.
*
* The default implementation should be used for distributions with
* Support DIST_UNBOUNDED, DIST_POSITIVE and DIST_PROPORTION. If
* the Support is DIST_SPECIAL, this must be overloaded.
*/
virtual double l(std::vector<double const *> const ¶meters) const;
/**
* Upper limit of distribution, given parameters. If the
* distribution has no upper limit, this should return JAGS_POSINF.
*
* The default implementation should be used for distributions with
* Support DIST_UNBOUNDED, DIST_POSITIVE and DIST_PROPORTION. If
* the Support is DIST_SPECIAL, this must be overloaded.
*/
virtual double u(std::vector<double const *> const ¶meters) const;
/**
* Calculates log probability density of the distribution
*/
virtual double logDensity(double x, PDFType type,
std::vector<double const *> const ¶meters,
double const *lbound, double const *ubound)
const = 0;
/**
* Draws a random sample
*/
virtual double randomSample(std::vector<double const *> const ¶meters,
double const *lbound, double const *ubound,
RNG *rng) const = 0;
/**
* Generate a deterministic value with reasonably high density
* (e.g. mean, median, mode ...)
*/
virtual double typicalValue(std::vector<double const *> const ¶meters,
double const *lbound, double const *ubound)
const = 0;
/**
* Checks whether the parameter values are valid
*/
virtual bool checkParameterValue(std::vector<double const *> const ¶ms)
const = 0;
/**
* Returns the number of degrees of freedom of the distribution.
* By default this is 1. For scalar distributions that are
* deterministic functions of the parameters, this must be overridden.
*/
virtual unsigned int df() const;
/**
* Returns a Monte Carlo estimate of the Kullback-Leibler divergence
* between distributions with two different parameter values. This
* is done by drawing random samples from the distribution with the
* first set of parameters and then calculating the log-likelihood
* ratio with respect to the second set of parameters.
*
* Only one lower and one upper bound is required, which is assumed
* common to both sets of parameters. This is because the
* Kullback-Leibler divergence is infinite between two bounded
* distributions if they do not share the same bounds.
*
* @param par1 First set of parameters
* @param par2 Second set of parameter values
* @param lower Pointer to lower bound (NULL if there is no lower bound)
* @param upper Pointer to upper bound (NULL if there is no upper bound)
* @param rng Random number generator
* @param nrep Number of replicates on which to base the estimate
*/
double KL(std::vector<double const *> const &par1,
std::vector<double const *> const &par2,
double const *lower, double const *upper,
RNG *rng, unsigned int nrep) const;
/**
* Returns the Kullback-Leibler divergence between distributions
* with two different parameter values.
*
* This is a virtual function that must be overloaded for any
* distribution that allows exact Kullback-Leibler divergence
* calculations. The default method returns JAGS_NA, indicating that
* the method is not implemented.
*/
virtual double KL(std::vector<double const *> const &par1,
std::vector<double const *> const &par2) const;
};
} /* namespace jags */
#endif /* SCALAR_DIST_H_ */
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