# main program random.seed(12345) # seed or the random number generator # loads data from file data = loadtxt(os.path.join(datadir,'count.txt'), skiprows = 1) yvec = data[:, 0] xmat = data[:, 1:data.shape[1]] xmat = hstack([ones((data.shape[0], 1)), xmat]) data ={'yvec':yvec, 'xmat':xmat} # use bayesian regression to initialise bayesreg = BayesRegression(yvec, xmat) sig, beta0 = bayesreg.posterior_mean() init_beta, info = leastsq(minfunc, beta0, args = (yvec, xmat)) data['betaprec'] =-llhessian(data, init_beta) scale = linalg.inv(data['betaprec']) # Initialise the random walk MH algorithm samplebeta = RWMH(posterior, scale, init_beta, 'beta') ms = MCMC(20000, 4000, data, [samplebeta], loglike = (logl, xmat.shape[1], 'yvec')) ms.sampler() ms.output() ms.plot('beta')
sum = zeros((kreg, kreg)) for i in xrange(nobs): sum = sum + lamb[i] * outer(store['xmat'][i], store['xmat'][i]) return -sum # main program random.seed(12345) # seed or the random number generator data = loadtxt(os.path.join(datadir,'count.txt'), skiprows = 1) # loads data from file yvec = data[:, 0] xmat = data[:, 1:data.shape[1]] xmat = hstack([ones((data.shape[0], 1)), xmat]) data ={'yvec':yvec, 'xmat':xmat} bayesreg = BayesRegression(yvec, xmat) # use bayesian regression to initialise # nonlinear least squares algorithm sig, beta0 = bayesreg.posterior_mean() init_beta, info = leastsq(minfunc, beta0, args = (yvec, xmat)) data['betaprec'] =-llhessian(data, init_beta) scale = linalg.inv(data['betaprec']) samplebeta = RWMH(posterior, scale, init_beta, 'beta') ms = MCMC(20000, 4000, data, [samplebeta], loglike = (logl, xmat.shape[1], 'yvec')) ms.sampler() ms.output(filename='example1c.out') ms.plot('beta', filename='ex_loglinear.pdf') # ms.CODAoutput('beta') # ms.plot('beta', elements = [0], plottypes ="trace", filename ="xx.pdf") # ms.plot('beta', elements = [0], plottypes ="density", filename ="xx.png") ## ms.plot('beta', elements = [0], plottypes ="acf", filename ="yy.ps")
priorreg = ("g_prior", zeros(kreg), 1000.0) regs = BayesRegression(yvec, xmat, prior=priorreg) """A dictionary is set up. The contents of the dictionary will be available for use for by the functions that make up the MCMC sampler. Note that we pass in storage space as well as the class intance used to sample the regression from.""" data = {"yvec": yvec, "xmat": xmat, "regsampler": regs} U = spmatrix.ll_mat(nobs, nobs, 2 * nobs - 1) U.put(1.0, range(0, nobs), range(0, nobs)) data["yvectil"] = zeros(nobs) data["xmattil"] = zeros((nobs, kreg)) data["Upper"] = U # Use Bayesian regression to initialise MCMC sampler bayesreg = BayesRegression(yvec, xmat) sig, beta = bayesreg.posterior_mean() simsigbeta = CFsampler(WLS, [sig, beta], ["sigma", "beta"]) rho = 0.9 simrho = SliceSampler([post_rho], 0.1, 5, rho, "rho") blocks = [simrho, simsigbeta] loglikeinfo = (loglike, kreg + 2, "yvec") ms = MCMC(10000, 2000, data, blocks, loglike=loglikeinfo) ms.sampler() ms.output() ms.plot("rho")
to sample the regression from.""" data ={'yvec':yvec, 'xmat':xmat, 'regsampler':regs} U = spmatrix.ll_mat(nobs, nobs, 2 * nobs - 1) U.put(1.0, range(0, nobs), range(0, nobs)) data['yvectil'] = zeros(nobs) data['xmattil'] = zeros((nobs, kreg)) data['Upper'] = U # Use Bayesian regression to initialise MCMC sampler bayesreg = BayesRegression(yvec, xmat) sig, beta = bayesreg.posterior_mean() simsigbeta = CFsampler(WLS, [sig, beta], ['sigma', 'beta']) scale = 0.002 # tuning parameter for RWMH rho = 0.9 ##rho = [1] ## to test exception handling # simrho = RWMH(post_rho, scale, rho, 'rho') simrho = SliceSampler([post_rho], 0.1, 5, rho, 'rho') #simrho = OBMC(post_rho, 3, scale, rho, 'rho') # simrho = MH(gencand, post_rho, probcandgprev, probprevgcand, rho, 'rho') blocks = [simrho, simsigbeta] loglikeinfo = (loglike, kreg + 2, 'yvec') ms = MCMC(10000, 2000, data, blocks, loglike = loglikeinfo) ms.sampler() ms.output() #ms.plot('sigbeta') ms.plot('rho', filename ='rho') ms.CODAoutput(parameters = ['rho'])