Merge pull request #232 from stan-dev/avehtari-patch-1

Some statistical terminology fixes

Author: mitzimorris

jupyter/sum-to-zero/sum_to_zero_evaluation.qmd

@@ -167,7 +167,7 @@ The spatial models are taken from the a set of notebooks available from GitHub r
 In this section we consider a model which estimates per-demographic disease prevalence rates for a population.
 The model is taken from the Gelman and Carpenter, 2020
 [Bayesian Analysis of Tests with Unknown Specificity and Sensitivity](https://doi.org/10.1111/rssc.12435).
-It combines a model for multilevel regression and post-stratification with a likelihood that
+It combines a model for multilevel regression and post-stratification with a model that
 accounts for test sensitivity and specificity.
 
 The data consists of:
@@ -221,7 +221,7 @@ transformed parameters {
   vector[N] p_sample = p * sens + (1 - p) * (1 - spec);
 }
 model {
-  pos_tests ~ binomial(tests, p_sample);  // likelihood
+  pos_tests ~ binomial(tests, p_sample);  // data model
   // scale normal priors on sum_to_zero_vectors
   beta_age ~ normal(0, s_age * sigma_age);
   beta_eth ~ normal(0, s_eth * sigma_eth);
@@ -240,7 +240,7 @@ In the `generated quantities` block we use Stan's PRNG functions to populate
  vthe true weights for the categorical coefficient vectors, and the relative percentages
 of per-category observations.
 Then we use a set of nested loops to generate the data for each demographic,
-using the PRNG equivalent of the model likelihood.
+using the PRNG equivalent of the data model.
 
 The full data-generating program is in file [gen_binomial_4_preds.stan](https://github.com/stan-dev/example-models/tree/master/jupyter/sum-to-zero/stan/binomial_4_preds_ozs.stan).
 The helper function `simulate_data` in file `utils.py` sets up the data-generating program
@@ -249,7 +249,7 @@ observations per category, and baseline disease prevalence, test specificity and
 This allows us to create datasets for large and small populations
 and for finer or more coarse-grained sets of categories.
 The larger the number of strata overall, the more observations are needed to get good coverage.
-Because the modeled data `pos_tests` is generated according to the Stan model's likelihood,
+Because the modeled data `pos_tests` is generated according to the Stan model,
 the model is a priori well-specified with respect to the data.
 
 
@@ -473,15 +473,15 @@ In the case of lots of observations and only a few categories they do a better j
 * In almost all cases, estimates for each parameter are the same across implementations to 2 significant figures.
 In a few cases they are off by 0.01; where they are off, the percentage of observations for that parameter is correspondingly low.
 
-* The `sum_to_zero_vector` implementation has the highest number of effective samples per second,
+* The `sum_to_zero_vector` implementation has the highest effective sample size per second,
 excepting a few individual parameters for which the hard sum-to-zero performs equally well.
 
 
-#### Model efficiency
+#### Sampling efficiency
 
-Model efficiency is measured by iterations per second, however, as the draws from the MCMC sampler
-may be correlated, we need to compute the number of effective samples across all chains
-divided by the total sampling time - this is *ESS_bulk/s*, the effective samples per second.
+Sampling efficiency is measured by iterations per second, however, as the draws from the MCMC sampler
+may be correlated, we need to compute the effective sample size across all chains
+divided by the total sampling time - this is *ESS_bulk/s*, the effective sample size per second.
 The following table shows the average runtime for 100 runs
 of each of the three models on large and small datasets.
 This data was generated by script `eval_efficiencies.py`.
@@ -784,7 +784,7 @@ brklyn_qns_data = {"N":brklyn_qns_gdf.shape[0],
 
 #### Model fitting
 
-The BYM2 model requires many warmup iterations in order to reach convergence for all parameters,
+The BYM2 model requires many warmup iterations in order to MCMC to converge for all parameters,
 including hyperparameters `rho` and `sigma`.
 We run all three models using the same seed, in order to make the initial parameters as similar
 as possible.