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Ch. 3 correction: MCMC *not* exploring the space well likely exhibits high autocorr. #456

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Apr 23, 2019
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Ch. 3 correction: MCMC *not* exploring the space well likely exhibits high autocorr. #456

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2 changes: 1 addition & 1 deletion Chapter3_MCMC/Ch3_IntroMCMC_PyMC2.ipynb
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Expand Up @@ -1091,7 +1091,7 @@
"\n",
"By the nature of the MCMC algorithm, we will always be returned samples that exhibit autocorrelation (this is because of the step `from your current position, move to a position near you`).\n",
"\n",
"A chain that is [Isn't meandering exploring?] exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n",
"A chain that is not exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n",
"\n",
"This does not imply that a converged MCMC has low autocorrelation. Hence low autocorrelation is not necessary for convergence, but it is sufficient. PyMC has a built-in autocorrelation plotting function in the `Matplot` module. "
]
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2 changes: 1 addition & 1 deletion Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb
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Expand Up @@ -1073,7 +1073,7 @@
"\n",
"By the nature of the MCMC algorithm, we will always be returned samples that exhibit autocorrelation (this is because of the step `from your current position, move to a position near you`).\n",
"\n",
"A chain that is [Isn't meandering exploring?] exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n",
"A chain that is not exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n",
"\n",
"This does not imply that a converged MCMC has low autocorrelation. Hence low autocorrelation is not necessary for convergence, but it is sufficient. PyMC3 has a built-in autocorrelation plotting function in the `plots` module. "
]
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2 changes: 1 addition & 1 deletion Chapter3_MCMC/Ch3_IntroMCMC_TFP.ipynb
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Expand Up @@ -1909,7 +1909,7 @@
"\n",
"By the nature of the MCMC algorithm, we will always be returned samples that exhibit autocorrelation (this is because of the step `from your current position, move to a position near you`).\n",
"\n",
"A chain that is [Isn't meandering exploring?] exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n",
"A chain that is not exploring the space well will exhibit very high autocorrelation. Visually, if the trace seems to meander like a river, and not settle down, the chain will have high autocorrelation.\n",
"\n",
"This does not imply that a converged MCMC has low autocorrelation. Hence low autocorrelation is not necessary for convergence, but it is sufficient. TFP has a built-in autocorrelation tools as well. "
]
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