/usr/include/JAGS/sampler/TemperedMetropolis.h is in jags 4.1.0-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 | #ifndef MIX_METHOD_H_
#define MIX_METHOD_H_
#include <sampler/Metropolis.h>
#include <sampler/StepAdapter.h>
#include <vector>
namespace jags {
class StepAdapter;
/**
* @short Metropolis-Hastings sampling method with tempered transitions
*
* TemperedMetropolis implements a random-walk Metropolis-Hastings
* method with tempered transitions (Neal, R. Sampling from multimodal
* distributions using tempered transitions, Statistics and Computing,
* vol 6, pp 353-355, 1996).
*
* A tempered log density at temperature T is derived from the target
* log density L by replacing L with L/T. For large T, the tempered
* density is flattened, converging to the prior density as T tends to
* infinity.
*
* In a tempered transition, a new Metropolis-Hasting proposal is
* generated by a sequence of Metropolis-Hastings updates that are
* stationary with respect to a tempered density. In this sequence, T
* starts close to 1 increases to a maximum (e.g. 1000) and then
* decreases to 1 again. Tempering allows the proposal to jump to a
* new local mode of the target density by tunnelling, at high
* temperature, through a region of low density.
*
* Three elements are essential for successful tempered updates. The
* first is a sufficiently high maximum temperature. Without this,
* tunnelling can never occur between well-separated modes of the
* target density. The second element is a sufficiently small
* temperature increment between levels. If the increase is too
* large, then the proposal generated by the tempered updates has a
* low acceptance probability. The third element is a sufficient
* number of replicates at each temperature level. In
* TemperedMetropolis the updates at each level are tuned to have an
* acceptance probability converging to 0.234. Several replicate
* updates are therefore required to guarantee that a M-H jump takes
* place. Without this, the proposal risks getting stuck in a region
* of low probability. These elements are controlled by the parameters
* max_level, max_temp, and nrep respectively.
*/
class TemperedMetropolis : public Metropolis
{
const int _max_level;
const unsigned int _nrep;
const std::vector<double> _pwr;
int _t;
int _tmax;
std::vector<StepAdapter*> _step_adapter;
double _pmean;
unsigned int _niter;
void temperedUpdate(RNG *rng, double &log_prior0, double &log_likelihood0,
std::vector<double> &value0);
public:
/**
* Constructor.
*
* @param nlevel Number of levels between minimum and maximum temperature
*
* @param max_temp Maximum temperature
*
* @param nrep Number of Metropolis-Hastings updates to do at each
* level
*/
TemperedMetropolis(std::vector<double> const &value,
int nlevel, double max_temp, unsigned int nrep);
~TemperedMetropolis();
/**
* Updates the current value using tempered transitions.
*/
void update(RNG *rng);
/**
* Modifies the step size at each temperature level to achieve the
* target acceptance probability using a noisy gradient algorithm
*
* When the mean acceptance probability is within the target
* range, the temperature is increased up to max_temp.
*
* @param prob acceptance probability at current update
*/
void rescale(double prob);
/**
* Checks whether the maximum temperature has been reached.
*/
bool checkAdaptation() const;
/**
* The target density is assumed to be the product of a prior density
* and a likelihood. Only the likelihood part of the density is
* tempered. This may be necessary in order to ensure that the
* tempered distribution can be normalized.
*
* This function returns the log of the prior density function.
*/
virtual double logPrior() const = 0;
/**
* Returns the log of the likelihood.
*/
virtual double logLikelihood() const = 0;
/**
* Modifies the given value vector in place by adding an
* independent normal increment to each element. It can be
* overridden to provide non-normal increments, or a random walk
* on some transformed scale (but see RMetropolis#logJacobian).
*
* Note that this function does not modify the value of the
* RWMetropolis object.
*/
virtual void step(std::vector<double> &value, double s, RNG *rng) const;
/**
* If the random walk takes place on a transformed scale
* (e.g. log, logistic), then the log density of the target
* distribution must be penalized by the log Jacobian of the
* transformation.
*
* This function calculates the log Jacobian at the given value.
*/
virtual double logJacobian(std::vector<double> const &value) const;
};
} /* namespace jags */
#endif /* MIX_METHOD_H_ */
|