Last updated: 2019-08-16

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Knit directory: lieb/

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html d58e1a6 hillarykoch 2019-08-16 resource data
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Rmd 330ef60 hillarykoch 2019-07-18 reorder mcmc section
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html 228123d hillarykoch 2019-07-17 update up to obtaining the hyperparameters
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Rmd 50cf23e hillarykoch 2019-07-17 make skeleton
html 50cf23e hillarykoch 2019-07-17 make skeleton

—This is likely the most time-consuming step of LIEB analysis—

The final step of LIEB involves doing inference on the parsimonious Gaussian mixture using MCMC. MCMC is an iterative method, and thus the user needs to specify how many iterations to use. We recommend running a quick pilot analysis–say, for 10 iterations. This pilot analysis will give a good idea of how long an analysis will need to run for a given larger number of iterations (say, 20,000 iterations).

You can run an mcmc simply with the function run_mcmc(). This function calls a script written in Julia, and executes everything at the default settings in the LIEB methodology. The user needs to provide 4 arguments:

  1. dat: the input data you’ve been using throughout the analysis

  2. hyp: the hyperparameter values estimated in the previous step

  3. nstep: number of MCMC iterations to run

  4. retained_classes: the parsimonious list of candidate latent classes, after finally filtering out by prior weights as done in the previous step

First, we load in our data, list of candidate latent classes, and estimated hyperparameters.

data("sim")
load("output/hyperparameters.Rdata")
retained_classes <- readr::read_tsv("output/retained_classes.txt", col_names = FALSE)

Now we are ready to launch an MCMC:

results <- run_mcmc(sim$data, hyp =  hyp, nstep = 100, retained_classes = retained_classes)
Running the MCMC...100%|████████████████████████████████| Time: 0:00:33

The object results contains 3 objects:

  1. chain: the estimate parameters over the course of nstep iterations

  2. acceptane_rate_chain: an \(M\times\)nstep matrix of the acceptance rates for each cluster covariance. The proposals for each cluster are adaptively tuned such that the acceptance rates converge to about 0.3

  3. tune_df_chain: the tuning degrees of freedom across the chain, adjusted to yield optimal acceptance rates

Functionality for processing the parameters estimates has not yet been introduced into LIEB.