updating accept_reject value

docs/search.json

@@ -70,7 +70,7 @@
     "href": "metropolis-hastings.html#coding-the-metropolis-hastings-in-practice",
     "title": "Metropolis Hastings",
     "section": "Coding the Metropolis Hastings in practice",
-    "text": "Coding the Metropolis Hastings in practice\n\nPart 1: sampling from a normal distribution\nGiven a \\(p(x) = N(2, 0.5)\\) how would draw samples using the Metropolis-Hastings algorithm?\n\nChoose proposal value\nEvaluate probability ratio\nAccept-Reject\nincrement step\n\n\nDefine probability density function\n\nimport numpy as np\nfrom scipy.stats import norm\n\ndef prob(x):\n  return norm.pdf(x, 2, 0.5)\n\n\n\nDefine proposal distribution\n\ndef proposal(x):\n  return norm.rvs(x, 1, 1)[0]\n\n\n\nInitialise sampler\n\ncurrent = 0.0\nsamples = [current]\n\n\n\nSample from distribution\n\nfor i in range(10000):\n    prop = proposal(current)\n    accept_reject = prob(prop)/prob(current)\n    if accept_reject > 1:\n        samples.append(prop)\n        current = prop\n    else:\n        cutoff = np.random.rand(1)[0]\n        if accept_reject > cutoff:\n            samples.append(prop)\n            current = prop\n\n\n\nPlot distribution\n\nimport matplotlib.pyplot as plt\nplt.hist(samples)\n\n(array([   4.,   23.,  158.,  627., 1159., 1379., 1029.,  476.,  113.,\n          17.]),\n array([-0.03049052,  0.34894132,  0.72837315,  1.10780499,  1.48723683,\n         1.86666866,  2.2461005 ,  2.62553234,  3.00496418,  3.38439601,\n         3.76382785]),\n <BarContainer object of 10 artists>)\n\n\n\n\n\n\n\n\n\n\n\nTrace plot\n\ndraw = [draw for draw, _ in enumerate(samples)]\nplt.plot(draw, samples)\n\n\n\n\n\n\n\n\n\n\n\nPart 2: determining mean and standard deviation from data\nI suggest using logs due to numerical issues. Here’s an example function which you can use to evaluate the probability of the data.\n\ndef eval_prob(data, mu, sigma):\n    return np.log(norm.pdf(data,mu,sigma)).sum()\n\nHere’s also a multivariate random number generator to generate proposals.\n\ndef proposal_multi(mu, sigma):\n    mean = [mu, sigma]\n    cov = [[0.2, 0], [0, 0.2]]  # diagonal covariance\n    return np.random.multivariate_normal(mean, cov, 1)[0]\n\nHere is how you’d call the proposal function\nprop_mu, prop_sigma = proposal_multi(current_mu, current_sigma)\nYou should sample a 95% interval including a \\(\\mu = 5\\) and a \\(\\sigma = 0.2\\). This may be difficult at first to sample and I would recommend initialising at these values.",
+    "text": "Coding the Metropolis Hastings in practice\n\nPart 1: sampling from a normal distribution\nGiven a \\(p(x) = N(2, 0.5)\\) how would draw samples using the Metropolis-Hastings algorithm?\n\nChoose proposal value\nEvaluate probability ratio\nAccept-Reject\nincrement step\n\n\nDefine probability density function\n\nimport numpy as np\nfrom scipy.stats import norm\n\ndef prob(x):\n  return norm.pdf(x, 2, 0.5)\n\n\n\nDefine proposal distribution\n\ndef proposal(x):\n  return norm.rvs(x, 1, 1)[0]\n\n\n\nInitialise sampler\n\ncurrent = 0.0\nsamples = [current]\n\n\n\nSample from distribution\n\nfor i in range(10000):\n    prop = proposal(current)\n    accept_reject = prob(prop)/prob(current)\n    if accept_reject > 1:\n        samples.append(prop)\n        current = prop\n    else:\n        cutoff = np.random.rand(1)[0]\n        if accept_reject > cutoff:\n            samples.append(prop)\n            current = prop\n\n\n\nPlot distribution\n\nimport matplotlib.pyplot as plt\nplt.hist(samples)\n\n(array([   6.,   30.,  204.,  606., 1071., 1257., 1130.,  536.,  161.,\n          13.]),\n array([0.        , 0.36895147, 0.73790294, 1.10685441, 1.47580588,\n        1.84475735, 2.21370882, 2.58266029, 2.95161176, 3.32056323,\n        3.6895147 ]),\n <BarContainer object of 10 artists>)\n\n\n\n\n\n\n\n\n\n\n\nTrace plot\n\ndraw = [draw for draw, _ in enumerate(samples)]\nplt.plot(draw, samples)\n\n\n\n\n\n\n\n\n\n\n\nPart 2: determining mean and standard deviation from data\nI suggest using logs due to numerical issues. Here’s an example function which you can use to evaluate the probability of the data.\n\ndef eval_prob(data, mu, sigma):\n    return np.log(norm.pdf(data,mu,sigma)).sum()\n\nHere’s also a multivariate random number generator to generate proposals.\n\ndef proposal_multi(mu, sigma):\n    mean = [mu, sigma]\n    cov = [[0.2, 0], [0, 0.2]]  # diagonal covariance\n    return np.random.multivariate_normal(mean, cov, 1)[0]\n\nHere is how you’d call the proposal function\nprop_mu, prop_sigma = proposal_multi(current_mu, current_sigma)\nthe accept_reject probability should also be updated to account for the log-probability\naccept_reject = np.exp(prob_prop - prob_current)\nYou should sample a 95% interval including a \\(\\mu = 5\\) and a \\(\\sigma = 0.2\\). This may be difficult at first to sample and I would recommend initialising at these values.",
     "crumbs": [
       "Course materials",
       "Metropolis Hastings"