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Simple Gibbs sampler in cython, adapted from http://bit.ly/J3vP63
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''' | |
Gibbs sampler for function: | |
f(x,y) = x x^2 \exp(-xy^2 - y^2 + 2y - 4x) | |
using conditional distributions: | |
x|y \sim Gamma(3, y^2 +4) | |
y|x \sim Normal(\frac{1}{1+x}, \frac{1}{2(1+x)}) | |
''' | |
cimport cython | |
import numpy as np | |
from numpy cimport * | |
cdef extern from "math.h": | |
double sqrt(double) | |
cdef extern from "gsl/gsl_rng.h": | |
ctypedef struct gsl_rng_type | |
ctypedef struct gsl_rng | |
gsl_rng_type *gsl_rng_mt19937 | |
gsl_rng *gsl_rng_alloc(gsl_rng_type * T) nogil | |
cdef extern from "gsl/gsl_randist.h": | |
double gamma "gsl_ran_gamma"(gsl_rng * r,double,double) | |
double gaussian "gsl_ran_gaussian"(gsl_rng * r,double) | |
cdef gsl_rng *r = gsl_rng_alloc(gsl_rng_mt19937) | |
@cython.boundscheck(False) | |
def gibbs(int N=20000,int thin=500): | |
cdef: | |
double x=0 | |
double y=0 | |
int i, j | |
ndarray[float64_t, ndim=2] samples | |
samples = np.empty((N,thin)) | |
for i from 0 <= i < N: | |
for j from 0 <= j < thin: | |
x = gamma(r,3,1.0/(y*y+4)) | |
y = gaussian(r,1.0/sqrt(x+1)) | |
samples[i,0] = x | |
samples[i,1] = y | |
return samples |
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