diff --git a/README.Rmd b/README.Rmd
index 53ec3d2..0fa5e79 100755
--- a/README.Rmd
+++ b/README.Rmd
@@ -41,6 +41,9 @@ library(pbapply)
 library(urbnmapr)
 library(ggrepel)
 library(DT)
+
+#  source('scripts/model/final_2016.R');source('scripts/model/final_2012.R');source('scripts/model/final_2008.R');beepr::beep(2);
+
 ```
 
 
@@ -57,7 +60,7 @@ start_date <- as.Date("2008-03-01") # Keeping all polls after March 1, 2016
 evs_df <- read_csv('data/2012.csv')
 
 # will read in the most recent back-test for 2008
-out  <- read_rds(sprintf('models/backtest_2008/stan_model_%s.rds',RUN_DATE))
+out  <- read_rds(sprintf('models/backtest_2008/stan_model_%s_normal_final.rds',RUN_DATE))
 
 # get all the polling data
 #setwd(here("data/"))
@@ -208,8 +211,8 @@ pct_obama <- pblapply(1:dim(predicted_score)[3],
                       temp <- predicted_score[,,x]
                       
                       # put in tibble
-                      tibble(low = apply(temp,2,function(x){(quantile(x,0.05))}),
-                             high = apply(temp,2,function(x){(quantile(x,0.95))}),
+                      tibble(low = apply(temp,2,function(x){(quantile(x,0.025))}),
+                             high = apply(temp,2,function(x){(quantile(x,0.975))}),
                              mean = apply(temp,2,function(x){(mean(x))}),
                              prob = apply(temp,2,function(x){(mean(x>0.5))}),
                              state = x) 
@@ -238,8 +241,8 @@ pct_obama_natl <- pblapply(1:dim(predicted_score)[2],
 
 pct_obama_natl <- pct_obama_natl %>%
   group_by(t) %>%
-  summarise(low = quantile(natl_vote,0.05),
-            high = quantile(natl_vote,0.95),
+  summarise(low = quantile(natl_vote,0.025),
+            high = quantile(natl_vote,0.975),
             mean = mean(natl_vote),
             prob = mean(natl_vote > 0.5)) %>%
   mutate(state = '--')
@@ -251,7 +254,7 @@ pct_obama <- pct_obama %>%
 
 # look
 ex_states <- c('IA','FL','OH','WI','MI','PA','AZ','NC','NH','TX','GA','MN')
-pct_obama %>% filter(t == RUN_DATE,state %in% c(ex_states,'--')) %>% mutate(se = (high - mean)/1.68) %>% dplyr::select(-t)
+pct_obama %>% filter(t == RUN_DATE,state %in% c(ex_states,'--')) %>% mutate(se = (high - mean)/1.96) %>% dplyr::select(-t)
 
 map.2008.gg <- urbnmapr::states %>%
   left_join(pct_obama %>% filter(t == max(t)) %>%
@@ -473,6 +476,32 @@ final_evs.2008.gg <-  ggplot(final_evs,aes(x=dem_ev,
          subtitle=sprintf("p(dem win) = %s | full stan model",round(mean(final_evs$dem_ev>=270),3)))
 
 
+
+# get the p value of the actual results
+p_values_2008 <- draws %>% 
+  filter(state != '--',
+         t == election_day) %>% 
+  left_join(politicaldata::pres_results %>% filter(year == 2008) %>%
+              mutate(actual = dem/(dem+rep)) %>%
+              dplyr::select(state,actual)) %>%
+  group_by(state) %>%
+  summarise(high = quantile(pct_obama, 0.975),
+            low = quantile(pct_obama, 0.025),
+            mean = mean(pct_obama),
+            prob = mean(pct_obama > 0.5),
+            actual = unique(actual),
+            p_value = 
+           ( 2*sum(pct_obama < actual) + 1 ) /
+           ( 2 * n() + 2) #,
+           # p_value_2 = mean(ifelse(actual >= mean(pct_obama),
+           #                         pct_obama >= actual,
+           #                         pct_obama <= actual))
+           ) %>%
+  arrange(p_value) %>% 
+  mutate(year = 2008,
+         outside_ci = ifelse(actual > high | actual < low, TRUE,FALSE))
+
+
 ```
 
 #### Map
@@ -547,7 +576,7 @@ start_date <- as.Date("2012-03-01") # Keeping all polls after March 1, 2016
 evs_df <- read_csv('data/2012.csv')
 
 # will read in the most recent back-test for 2012
-out  <- read_rds(sprintf('models/backtest_2012/stan_model_%s.rds',RUN_DATE))
+out  <- read_rds(sprintf('models/backtest_2012/stan_model_%s_normal_final.rds',RUN_DATE))
 
 # get all the polling data
 #setwd(here("data/"))
@@ -693,8 +722,8 @@ pct_obama <- pblapply(1:dim(predicted_score)[3],
                       temp <- predicted_score[,,x]
                       
                       # put in tibble
-                      tibble(low = apply(temp,2,function(x){(quantile(x,0.05))}),
-                             high = apply(temp,2,function(x){(quantile(x,0.95))}),
+                      tibble(low = apply(temp,2,function(x){(quantile(x,0.025))}),
+                             high = apply(temp,2,function(x){(quantile(x,0.975))}),
                              mean = apply(temp,2,function(x){(mean(x))}),
                              prob = apply(temp,2,function(x){(mean(x>0.5))}),
                              state = x) 
@@ -723,8 +752,8 @@ pct_obama_natl <- pblapply(1:dim(predicted_score)[2],
 
 pct_obama_natl <- pct_obama_natl %>%
   group_by(t) %>%
-  summarise(low = quantile(natl_vote,0.05),
-            high = quantile(natl_vote,0.95),
+  summarise(low = quantile(natl_vote,0.025),
+            high = quantile(natl_vote,0.975),
             mean = mean(natl_vote),
             prob = mean(natl_vote > 0.5)) %>%
   mutate(state = '--')
@@ -736,7 +765,7 @@ pct_obama <- pct_obama %>%
 
 # look
 ex_states <- c('IA','FL','OH','WI','MI','PA','AZ','NC','NH','TX','GA','MN')
-pct_obama %>% filter(t == RUN_DATE,state %in% c(ex_states,'--')) %>% mutate(se = (high - mean)/1.68) %>% dplyr::select(-t)
+pct_obama %>% filter(t == RUN_DATE,state %in% c(ex_states,'--')) %>% mutate(se = (high - mean)/1.96) %>% dplyr::select(-t)
 
 map.2012.gg <- urbnmapr::states %>%
   left_join(pct_obama %>% filter(t == max(t)) %>%
@@ -962,6 +991,33 @@ final_evs.2012.gg <-  ggplot(final_evs,aes(x=dem_ev,
          subtitle=sprintf("p(dem win) = %s | full stan model",round(mean(final_evs$dem_ev>=270),3)))
 
 
+
+
+# get the p value of the actual results
+p_values_2012 <- draws %>% 
+  filter(state != '--',
+         t == election_day) %>% 
+  left_join(politicaldata::pres_results %>% filter(year == 2012) %>%
+              mutate(actual = dem/(dem+rep)) %>%
+              dplyr::select(state,actual)) %>%
+  group_by(state) %>%
+  summarise(high = quantile(pct_obama, 0.975),
+            low = quantile(pct_obama, 0.025),
+            mean = mean(pct_obama),
+            prob = mean(pct_obama > 0.5),
+            actual = unique(actual),
+            p_value = 
+           ( 2*sum(pct_obama < actual) + 1 ) /
+           ( 2 * n() + 2) #,
+           # p_value_2 = mean(ifelse(actual >= mean(pct_obama),
+           #                         pct_obama >= actual,
+           #                         pct_obama <= actual))
+           ) %>%
+  arrange(p_value) %>% 
+  mutate(year = 2012,
+         outside_ci = ifelse(actual > high | actual < low, TRUE,FALSE))
+
+
 ```
 
 
@@ -1038,7 +1094,7 @@ start_date <- as.Date("2016-03-01") # Keeping all polls after March 1, 2016
 evs_df <- read_csv('data/2012.csv')
 
 # will read in the most recent back-test for 2012
-out  <- read_rds(sprintf('models/stan_model_%s.rds',RUN_DATE))
+out  <- read_rds(sprintf('models/stan_model_%s_normal_final.rds',RUN_DATE))
 
 # get all the polling data
 #setwd(here("data/"))
@@ -1457,6 +1513,34 @@ final_evs.2016.gg <-  ggplot(final_evs,aes(x=dem_ev,
          subtitle=sprintf("p(dem win) = %s | full stan model",round(mean(final_evs$dem_ev>=270),3)))
 
 
+
+
+# get the p value of the actual results
+p_values_2016 <- draws %>% 
+  filter(state != '--',
+         t == election_day) %>% 
+  left_join(politicaldata::pres_results %>% filter(year == 2016) %>%
+              mutate(actual = dem/(dem+rep)) %>%
+              dplyr::select(state,actual)) %>%
+  group_by(state) %>%
+  summarise(high = quantile(pct_clinton, 0.975),
+            low = quantile(pct_clinton, 0.025),
+            mean = mean(pct_clinton),
+            prob = mean(pct_clinton > 0.5),
+            actual = unique(actual),
+            p_value = 
+           ( 2*sum(pct_clinton < actual) + 1 ) /
+           ( 2 * n() + 2) #,
+           # p_value_2 = mean(ifelse(actual >= mean(pct_clinton),
+           #                         pct_clinton >= actual,
+           #                         pct_clinton <= actual))
+           ) %>%
+  arrange(p_value) %>% 
+  mutate(year = 2016,
+         outside_ci = ifelse(actual > high | actual < low, TRUE,FALSE))
+
+
+
 ```
 
 
@@ -1523,7 +1607,7 @@ pct_clinton %>%
 
 ## Cumulative charts
 
-### Calibration plot
+### Probability calibration plot
 
 ```{r echo=F,message=F,warning=F}
 calibration_data <- model_v_actual.2016 %>% mutate(year = 2016) %>%
@@ -1551,6 +1635,63 @@ print(calibration.gg)
 ```
 
 
+### Confidence interval coverage
+
+```{r echo=F, message=FALSE, warning=FALSE}
+
+calibration_data <-  p_values_2016 %>% mutate(year = 2016) %>%
+        bind_rows(p_values_2012 %>% mutate(year = 2012) ) %>%
+        bind_rows(p_values_2008 %>% mutate(year = 2008)) %>%
+  filter(state != 'DC')
+
+
+# sum(calibration_data$outside_ci) / nrow(calibration_data)
+
+p_value_hist.gg <- ggplot(calibration_data, aes(x=p_value)) +
+  geom_histogram(binwidth = 0.01,aes(group=paste0(state,year))) +
+  geom_vline(xintercept = 0.025,col='red',linetype=2) +
+  geom_vline(xintercept = 0.975,col='red',linetype=2) +
+  scale_x_continuous(breaks=seq(0,1,0.05))  +
+  scale_y_continuous(breaks=seq(0,100,2))  +
+  theme_minimal() +
+  theme(panel.grid.minor = element_blank())
+
+
+preds_p_value_scatter.gg <- ggplot(calibration_data, aes(x=mean*100,y=actual*100)) +
+  geom_abline() +
+  # states inside the ci
+  geom_point(data =. %>% filter(!outside_ci),col='black') +
+  # states outside
+  geom_point(data = . %>% filter(outside_ci),col='red') +
+  geom_text_repel(data = . %>% filter(outside_ci),
+              aes(label=state),col='red',min.segment.length = 0) +
+  geom_segment(data = . %>% filter(outside_ci),
+               aes(x=mean*100,xend=mean*100,y=low*100,yend=high*100),col='red') +
+  facet_wrap(~year,ncol=3) +
+  theme_minimal() +
+  theme(panel.grid.minor = element_blank()) +
+  labs(
+    # title='Model with normal(0, sigma) distributions for polling error and state priors',
+    subtitle='Results in highlighted states fall outside our uncertainty intervals',
+    x='Predicted Democratic share of the two-party vote',
+    y='Actual Democratic share'
+    ) +
+  coord_cartesian(xlim=c(25,75),ylim=c(25,75))
+
+
+
+
+```
+
+```{r echo=F,message=F,warning=F,fig.width = 9, fig.asp = .42}
+print(preds_p_value_scatter.gg)
+```
+
+```{r echo=F,message=F,warning=F}
+print(p_value_hist.gg)
+```
+
+
 # Licence
 
 This software is published by _[The Economist](https://www.economist.com)_ under the [MIT licence](https://opensource.org/licenses/MIT). The data generated by _The Economist_ are available under the [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
diff --git a/README.md b/README.md
index bdfbbf8..4fb2034 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 State and national presidential election forecasting model
 ================
-Last update on Wednesday August 05, 2020 at 09:53 AM EDT
+Last update on Thursday October 15, 2020 at 12:29 PM EDT
 
 Code for a dynamic multilevel Bayesian model to predict US presidential
 elections. Written in R and Stan.
@@ -72,68 +72,68 @@ Here is a graphical summary of the model’s performance in 2008, 2012 and
 
 | outlet               | ev\_wtd\_brier | unwtd\_brier | states\_correct |
 | :------------------- | -------------: | -----------: | --------------: |
-| economist (backtest) |      0.0327228 |    0.0290726 |              49 |
+| economist (backtest) |      0.0321964 |    0.0289902 |              49 |
 
 ![](README_files/figure-gfm/unnamed-chunk-7-1.png)<!-- -->
 
-    ## [1] 0.02310776
+    ## [1] 0.02318035
 
 #### Predictions for each state
 
 | state |  mean |   low |  high |  prob |    se |
 | :---- | ----: | ----: | ----: | ----: | ----: |
-| NC    | 0.501 | 0.471 | 0.533 | 0.525 | 0.019 |
-| MO    | 0.509 | 0.479 | 0.539 | 0.693 | 0.018 |
-| FL    | 0.513 | 0.483 | 0.545 | 0.774 | 0.019 |
-| IN    | 0.483 | 0.454 | 0.513 | 0.178 | 0.018 |
-| AR    | 0.477 | 0.443 | 0.508 | 0.112 | 0.019 |
-| OH    | 0.524 | 0.495 | 0.554 | 0.908 | 0.018 |
-| VA    | 0.526 | 0.495 | 0.557 | 0.922 | 0.018 |
-| MT    | 0.474 | 0.445 | 0.505 | 0.082 | 0.018 |
-| GA    | 0.473 | 0.441 | 0.504 | 0.078 | 0.019 |
-| NV    | 0.531 | 0.500 | 0.562 | 0.949 | 0.018 |
-| WV    | 0.469 | 0.439 | 0.501 | 0.053 | 0.019 |
-| AZ    | 0.468 | 0.437 | 0.500 | 0.048 | 0.019 |
-| CO    | 0.533 | 0.502 | 0.564 | 0.956 | 0.018 |
-| LA    | 0.460 | 0.425 | 0.494 | 0.026 | 0.021 |
-| –     | 0.540 | 0.520 | 0.557 | 1.000 | 0.011 |
-| MS    | 0.456 | 0.423 | 0.490 | 0.019 | 0.020 |
-| TX    | 0.453 | 0.418 | 0.488 | 0.015 | 0.020 |
-| SD    | 0.452 | 0.421 | 0.483 | 0.005 | 0.019 |
-| NH    | 0.550 | 0.519 | 0.580 | 0.996 | 0.018 |
-| SC    | 0.449 | 0.416 | 0.482 | 0.005 | 0.020 |
-| ND    | 0.448 | 0.417 | 0.480 | 0.004 | 0.019 |
-| PA    | 0.553 | 0.524 | 0.583 | 0.998 | 0.018 |
-| TN    | 0.444 | 0.414 | 0.476 | 0.002 | 0.019 |
-| WI    | 0.557 | 0.526 | 0.586 | 0.999 | 0.017 |
-| KY    | 0.441 | 0.412 | 0.472 | 0.001 | 0.018 |
-| MN    | 0.559 | 0.528 | 0.588 | 0.999 | 0.017 |
-| NM    | 0.560 | 0.527 | 0.592 | 0.997 | 0.019 |
-| IA    | 0.561 | 0.531 | 0.591 | 1.000 | 0.018 |
-| MI    | 0.564 | 0.534 | 0.593 | 1.000 | 0.017 |
-| OR    | 0.571 | 0.542 | 0.600 | 1.000 | 0.017 |
-| KS    | 0.423 | 0.393 | 0.454 | 0.000 | 0.018 |
-| ME    | 0.583 | 0.552 | 0.614 | 1.000 | 0.018 |
-| WA    | 0.584 | 0.553 | 0.613 | 1.000 | 0.018 |
-| AK    | 0.416 | 0.383 | 0.449 | 0.000 | 0.020 |
-| NJ    | 0.590 | 0.559 | 0.620 | 1.000 | 0.018 |
-| AL    | 0.405 | 0.373 | 0.438 | 0.000 | 0.020 |
-| NE    | 0.402 | 0.370 | 0.434 | 0.000 | 0.019 |
-| DE    | 0.615 | 0.582 | 0.648 | 1.000 | 0.020 |
-| CA    | 0.616 | 0.584 | 0.648 | 1.000 | 0.019 |
-| CT    | 0.618 | 0.586 | 0.649 | 1.000 | 0.018 |
-| MD    | 0.618 | 0.580 | 0.654 | 1.000 | 0.021 |
-| OK    | 0.378 | 0.347 | 0.411 | 0.000 | 0.020 |
-| IL    | 0.629 | 0.597 | 0.660 | 1.000 | 0.018 |
-| WY    | 0.366 | 0.335 | 0.396 | 0.000 | 0.018 |
-| MA    | 0.642 | 0.611 | 0.673 | 1.000 | 0.018 |
-| NY    | 0.646 | 0.617 | 0.675 | 1.000 | 0.017 |
-| ID    | 0.354 | 0.324 | 0.383 | 0.000 | 0.018 |
-| VT    | 0.654 | 0.623 | 0.683 | 1.000 | 0.018 |
-| UT    | 0.342 | 0.312 | 0.373 | 0.000 | 0.018 |
-| HI    | 0.662 | 0.628 | 0.697 | 1.000 | 0.021 |
-| RI    | 0.670 | 0.639 | 0.698 | 1.000 | 0.017 |
-| DC    | 0.933 | 0.920 | 0.944 | 1.000 | 0.007 |
+| NC    | 0.502 | 0.466 | 0.538 | 0.545 | 0.021 |
+| MO    | 0.509 | 0.474 | 0.543 | 0.704 | 0.020 |
+| FL    | 0.514 | 0.479 | 0.548 | 0.788 | 0.020 |
+| IN    | 0.484 | 0.448 | 0.519 | 0.179 | 0.021 |
+| AR    | 0.477 | 0.439 | 0.516 | 0.124 | 0.023 |
+| OH    | 0.524 | 0.489 | 0.559 | 0.917 | 0.021 |
+| MT    | 0.475 | 0.437 | 0.512 | 0.094 | 0.022 |
+| VA    | 0.527 | 0.491 | 0.564 | 0.917 | 0.022 |
+| GA    | 0.473 | 0.437 | 0.511 | 0.083 | 0.022 |
+| WV    | 0.470 | 0.434 | 0.507 | 0.058 | 0.022 |
+| AZ    | 0.468 | 0.429 | 0.507 | 0.055 | 0.023 |
+| NV    | 0.532 | 0.494 | 0.568 | 0.952 | 0.022 |
+| CO    | 0.533 | 0.496 | 0.571 | 0.961 | 0.022 |
+| –     | 0.539 | 0.517 | 0.562 | 1.000 | 0.013 |
+| LA    | 0.460 | 0.421 | 0.500 | 0.025 | 0.024 |
+| MS    | 0.457 | 0.418 | 0.497 | 0.019 | 0.024 |
+| TX    | 0.454 | 0.413 | 0.495 | 0.015 | 0.024 |
+| SD    | 0.453 | 0.413 | 0.492 | 0.008 | 0.023 |
+| SC    | 0.450 | 0.412 | 0.488 | 0.003 | 0.023 |
+| NH    | 0.550 | 0.514 | 0.587 | 0.998 | 0.022 |
+| ND    | 0.449 | 0.409 | 0.487 | 0.006 | 0.023 |
+| PA    | 0.554 | 0.518 | 0.588 | 0.999 | 0.020 |
+| TN    | 0.445 | 0.406 | 0.484 | 0.003 | 0.023 |
+| WI    | 0.557 | 0.521 | 0.592 | 0.999 | 0.021 |
+| KY    | 0.442 | 0.405 | 0.478 | 0.001 | 0.021 |
+| MN    | 0.559 | 0.523 | 0.595 | 1.000 | 0.021 |
+| NM    | 0.561 | 0.522 | 0.599 | 0.998 | 0.023 |
+| IA    | 0.562 | 0.526 | 0.597 | 1.000 | 0.021 |
+| MI    | 0.565 | 0.530 | 0.598 | 1.000 | 0.020 |
+| OR    | 0.572 | 0.535 | 0.608 | 1.000 | 0.021 |
+| KS    | 0.424 | 0.387 | 0.460 | 0.000 | 0.022 |
+| AK    | 0.417 | 0.377 | 0.458 | 0.000 | 0.024 |
+| WA    | 0.584 | 0.548 | 0.620 | 1.000 | 0.021 |
+| ME    | 0.584 | 0.545 | 0.620 | 1.000 | 0.021 |
+| NJ    | 0.590 | 0.552 | 0.627 | 1.000 | 0.022 |
+| AL    | 0.405 | 0.368 | 0.444 | 0.000 | 0.023 |
+| NE    | 0.402 | 0.365 | 0.440 | 0.000 | 0.023 |
+| DE    | 0.616 | 0.577 | 0.652 | 1.000 | 0.022 |
+| CA    | 0.617 | 0.578 | 0.653 | 1.000 | 0.022 |
+| MD    | 0.618 | 0.572 | 0.664 | 1.000 | 0.027 |
+| CT    | 0.619 | 0.579 | 0.655 | 1.000 | 0.022 |
+| OK    | 0.378 | 0.340 | 0.417 | 0.000 | 0.023 |
+| IL    | 0.629 | 0.591 | 0.663 | 1.000 | 0.020 |
+| WY    | 0.366 | 0.331 | 0.404 | 0.000 | 0.022 |
+| MA    | 0.642 | 0.603 | 0.680 | 1.000 | 0.023 |
+| ID    | 0.354 | 0.318 | 0.392 | 0.000 | 0.022 |
+| NY    | 0.647 | 0.610 | 0.683 | 1.000 | 0.021 |
+| VT    | 0.654 | 0.615 | 0.693 | 1.000 | 0.023 |
+| UT    | 0.343 | 0.306 | 0.381 | 0.000 | 0.023 |
+| HI    | 0.663 | 0.622 | 0.703 | 1.000 | 0.024 |
+| RI    | 0.670 | 0.633 | 0.706 | 1.000 | 0.021 |
+| DC    | 0.933 | 0.917 | 0.946 | 1.000 | 0.008 |
 
 ### 2012
 
@@ -166,70 +166,70 @@ Here is a graphical summary of the model’s performance in 2008, 2012 and
 | Silver/538            |             NA |    0.0091100 |              NA |
 | Jackman/Pollster      |             NA |    0.0097100 |              NA |
 | Desart/Holbrook       |             NA |    0.0160500 |              NA |
-| economist (backtest)  |      0.0320587 |    0.0188167 |              50 |
+| economist (backtest)  |      0.0324297 |    0.0193188 |              50 |
 | Intrade               |             NA |    0.0281200 |              NA |
 | Enten/Margin of Error |             NA |    0.0507500 |              NA |
 
 ![](README_files/figure-gfm/unnamed-chunk-15-1.png)<!-- -->
 
-    ## [1] 0.02241299
+    ## [1] 0.02247233
 
 #### Predictions for each state
 
 | state |  mean |   low |  high |  prob |    se |
 | :---- | ----: | ----: | ----: | ----: | ----: |
-| VA    | 0.504 | 0.475 | 0.533 | 0.589 | 0.017 |
-| FL    | 0.495 | 0.466 | 0.525 | 0.391 | 0.018 |
-| CO    | 0.506 | 0.476 | 0.535 | 0.620 | 0.017 |
-| –     | 0.509 | 0.490 | 0.529 | 0.785 | 0.012 |
-| OH    | 0.510 | 0.482 | 0.539 | 0.721 | 0.017 |
-| NH    | 0.513 | 0.483 | 0.543 | 0.768 | 0.018 |
-| NC    | 0.486 | 0.457 | 0.515 | 0.212 | 0.017 |
-| IA    | 0.515 | 0.486 | 0.544 | 0.793 | 0.017 |
-| NV    | 0.516 | 0.486 | 0.547 | 0.807 | 0.018 |
-| WI    | 0.521 | 0.491 | 0.551 | 0.884 | 0.018 |
-| PA    | 0.528 | 0.498 | 0.558 | 0.936 | 0.018 |
-| MN    | 0.534 | 0.504 | 0.564 | 0.971 | 0.018 |
-| MI    | 0.538 | 0.509 | 0.568 | 0.983 | 0.018 |
-| OR    | 0.539 | 0.508 | 0.571 | 0.979 | 0.019 |
-| MO    | 0.460 | 0.430 | 0.491 | 0.014 | 0.018 |
-| NM    | 0.540 | 0.509 | 0.572 | 0.983 | 0.019 |
-| IN    | 0.456 | 0.426 | 0.487 | 0.009 | 0.018 |
-| MT    | 0.453 | 0.423 | 0.484 | 0.004 | 0.018 |
-| AZ    | 0.453 | 0.421 | 0.483 | 0.006 | 0.018 |
-| GA    | 0.452 | 0.420 | 0.484 | 0.006 | 0.019 |
-| NJ    | 0.556 | 0.525 | 0.587 | 0.999 | 0.018 |
-| ME    | 0.558 | 0.527 | 0.589 | 0.999 | 0.018 |
-| SC    | 0.439 | 0.401 | 0.475 | 0.004 | 0.022 |
-| WA    | 0.562 | 0.531 | 0.591 | 1.000 | 0.017 |
-| CT    | 0.567 | 0.537 | 0.596 | 1.000 | 0.017 |
-| SD    | 0.431 | 0.398 | 0.465 | 0.000 | 0.020 |
-| ND    | 0.423 | 0.393 | 0.454 | 0.000 | 0.019 |
-| MS    | 0.420 | 0.382 | 0.458 | 0.000 | 0.023 |
-| TN    | 0.419 | 0.388 | 0.450 | 0.000 | 0.019 |
-| WV    | 0.416 | 0.384 | 0.452 | 0.000 | 0.021 |
-| CA    | 0.585 | 0.555 | 0.615 | 1.000 | 0.018 |
-| MA    | 0.588 | 0.559 | 0.617 | 1.000 | 0.017 |
-| TX    | 0.409 | 0.378 | 0.442 | 0.000 | 0.019 |
-| NE    | 0.408 | 0.377 | 0.438 | 0.000 | 0.018 |
-| LA    | 0.401 | 0.368 | 0.434 | 0.000 | 0.020 |
-| IL    | 0.600 | 0.569 | 0.629 | 1.000 | 0.018 |
-| KY    | 0.399 | 0.366 | 0.434 | 0.000 | 0.020 |
-| DE    | 0.602 | 0.565 | 0.638 | 1.000 | 0.021 |
-| KS    | 0.396 | 0.360 | 0.431 | 0.000 | 0.021 |
-| MD    | 0.607 | 0.573 | 0.639 | 1.000 | 0.019 |
-| RI    | 0.616 | 0.584 | 0.648 | 1.000 | 0.019 |
-| AR    | 0.384 | 0.352 | 0.418 | 0.000 | 0.020 |
-| AL    | 0.383 | 0.350 | 0.415 | 0.000 | 0.020 |
-| NY    | 0.620 | 0.590 | 0.649 | 1.000 | 0.017 |
-| AK    | 0.364 | 0.326 | 0.401 | 0.000 | 0.022 |
-| VT    | 0.660 | 0.628 | 0.693 | 1.000 | 0.019 |
-| HI    | 0.661 | 0.630 | 0.692 | 1.000 | 0.019 |
-| ID    | 0.332 | 0.301 | 0.364 | 0.000 | 0.019 |
-| OK    | 0.331 | 0.298 | 0.363 | 0.000 | 0.019 |
-| WY    | 0.313 | 0.281 | 0.347 | 0.000 | 0.020 |
-| UT    | 0.291 | 0.261 | 0.320 | 0.000 | 0.018 |
-| DC    | 0.903 | 0.884 | 0.920 | 1.000 | 0.010 |
+| VA    | 0.504 | 0.467 | 0.539 | 0.586 | 0.021 |
+| CO    | 0.505 | 0.471 | 0.541 | 0.617 | 0.021 |
+| FL    | 0.495 | 0.457 | 0.531 | 0.394 | 0.021 |
+| OH    | 0.510 | 0.474 | 0.545 | 0.704 | 0.021 |
+| –     | 0.510 | 0.489 | 0.533 | 0.773 | 0.014 |
+| NH    | 0.513 | 0.478 | 0.548 | 0.758 | 0.021 |
+| IA    | 0.514 | 0.480 | 0.550 | 0.785 | 0.021 |
+| NC    | 0.485 | 0.451 | 0.521 | 0.212 | 0.022 |
+| NV    | 0.516 | 0.478 | 0.554 | 0.797 | 0.022 |
+| WI    | 0.521 | 0.486 | 0.556 | 0.876 | 0.021 |
+| PA    | 0.528 | 0.492 | 0.563 | 0.937 | 0.021 |
+| MN    | 0.534 | 0.500 | 0.568 | 0.974 | 0.021 |
+| MI    | 0.537 | 0.502 | 0.572 | 0.979 | 0.021 |
+| OR    | 0.538 | 0.499 | 0.575 | 0.971 | 0.022 |
+| MO    | 0.460 | 0.424 | 0.496 | 0.015 | 0.022 |
+| NM    | 0.540 | 0.500 | 0.580 | 0.976 | 0.023 |
+| IN    | 0.455 | 0.420 | 0.492 | 0.006 | 0.022 |
+| MT    | 0.453 | 0.417 | 0.489 | 0.006 | 0.022 |
+| GA    | 0.452 | 0.413 | 0.492 | 0.009 | 0.024 |
+| AZ    | 0.452 | 0.414 | 0.491 | 0.009 | 0.023 |
+| NJ    | 0.556 | 0.518 | 0.593 | 0.999 | 0.022 |
+| ME    | 0.557 | 0.520 | 0.593 | 0.999 | 0.021 |
+| WA    | 0.561 | 0.526 | 0.596 | 1.000 | 0.021 |
+| SC    | 0.438 | 0.395 | 0.483 | 0.004 | 0.026 |
+| CT    | 0.567 | 0.529 | 0.603 | 1.000 | 0.022 |
+| SD    | 0.431 | 0.392 | 0.470 | 0.000 | 0.023 |
+| ND    | 0.422 | 0.385 | 0.461 | 0.000 | 0.023 |
+| MS    | 0.421 | 0.375 | 0.468 | 0.000 | 0.028 |
+| TN    | 0.419 | 0.383 | 0.458 | 0.000 | 0.023 |
+| WV    | 0.416 | 0.378 | 0.456 | 0.000 | 0.024 |
+| CA    | 0.585 | 0.549 | 0.622 | 1.000 | 0.022 |
+| MA    | 0.588 | 0.552 | 0.623 | 1.000 | 0.021 |
+| TX    | 0.409 | 0.372 | 0.448 | 0.000 | 0.023 |
+| NE    | 0.407 | 0.370 | 0.444 | 0.000 | 0.022 |
+| IL    | 0.599 | 0.562 | 0.635 | 1.000 | 0.021 |
+| LA    | 0.401 | 0.363 | 0.442 | 0.000 | 0.024 |
+| DE    | 0.602 | 0.555 | 0.646 | 1.000 | 0.026 |
+| KY    | 0.398 | 0.360 | 0.438 | 0.000 | 0.024 |
+| KS    | 0.395 | 0.355 | 0.435 | 0.000 | 0.024 |
+| MD    | 0.607 | 0.565 | 0.646 | 1.000 | 0.023 |
+| RI    | 0.616 | 0.575 | 0.654 | 1.000 | 0.023 |
+| AR    | 0.384 | 0.346 | 0.423 | 0.000 | 0.023 |
+| AL    | 0.383 | 0.342 | 0.423 | 0.000 | 0.024 |
+| NY    | 0.620 | 0.584 | 0.655 | 1.000 | 0.021 |
+| AK    | 0.363 | 0.320 | 0.407 | 0.000 | 0.026 |
+| VT    | 0.660 | 0.620 | 0.697 | 1.000 | 0.022 |
+| HI    | 0.661 | 0.620 | 0.699 | 1.000 | 0.023 |
+| ID    | 0.331 | 0.295 | 0.369 | 0.000 | 0.022 |
+| OK    | 0.330 | 0.293 | 0.369 | 0.000 | 0.023 |
+| WY    | 0.313 | 0.277 | 0.350 | 0.000 | 0.022 |
+| UT    | 0.291 | 0.257 | 0.326 | 0.000 | 0.021 |
+| DC    | 0.903 | 0.880 | 0.924 | 1.000 | 0.012 |
 
 ### 2016
 
@@ -257,7 +257,7 @@ Here is a graphical summary of the model’s performance in 2008, 2012 and
 
 | outlet               | ev\_wtd\_brier | unwtd\_brier | states\_correct |
 | :------------------- | -------------: | -----------: | --------------: |
-| economist (backtest) |      0.0746651 |    0.0520891 |              48 |
+| economist (backtest) |      0.0725679 |    0.0508319 |              48 |
 | 538 polls-plus       |      0.0928000 |    0.0664000 |              46 |
 | 538 polls-only       |      0.0936000 |    0.0672000 |              46 |
 | princeton            |      0.1169000 |    0.0744000 |              47 |
@@ -272,71 +272,77 @@ Here is a graphical summary of the model’s performance in 2008, 2012 and
 
 ![](README_files/figure-gfm/unnamed-chunk-23-1.png)<!-- -->
 
-    ## [1] 0.0274812
+    ## [1] 0.02724916
 
 #### Predictions for each state
 
 | state |  mean |   low |  high |  prob |    se |
 | :---- | ----: | ----: | ----: | ----: | ----: |
-| FL    | 0.497 | 0.458 | 0.536 | 0.436 | 0.023 |
-| NC    | 0.493 | 0.453 | 0.534 | 0.367 | 0.024 |
-| NV    | 0.509 | 0.468 | 0.549 | 0.667 | 0.023 |
-| –     | 0.514 | 0.483 | 0.543 | 0.831 | 0.017 |
-| PA    | 0.514 | 0.475 | 0.554 | 0.757 | 0.024 |
-| NH    | 0.514 | 0.474 | 0.555 | 0.746 | 0.024 |
-| OH    | 0.485 | 0.446 | 0.524 | 0.233 | 0.023 |
-| CO    | 0.516 | 0.476 | 0.556 | 0.773 | 0.024 |
-| MI    | 0.519 | 0.479 | 0.558 | 0.821 | 0.023 |
-| WI    | 0.521 | 0.481 | 0.559 | 0.860 | 0.022 |
-| IA    | 0.479 | 0.438 | 0.518 | 0.147 | 0.023 |
-| VA    | 0.523 | 0.482 | 0.564 | 0.862 | 0.025 |
-| MN    | 0.528 | 0.488 | 0.568 | 0.919 | 0.024 |
-| AZ    | 0.471 | 0.431 | 0.511 | 0.081 | 0.024 |
-| GA    | 0.470 | 0.431 | 0.511 | 0.076 | 0.024 |
-| NM    | 0.533 | 0.492 | 0.576 | 0.941 | 0.025 |
-| ME    | 0.542 | 0.501 | 0.583 | 0.977 | 0.024 |
+| FL    | 0.496 | 0.457 | 0.536 | 0.435 | 0.024 |
+| NV    | 0.508 | 0.467 | 0.548 | 0.652 | 0.024 |
+| NC    | 0.492 | 0.451 | 0.532 | 0.340 | 0.024 |
+| –     | 0.512 | 0.485 | 0.540 | 0.791 | 0.017 |
+| NH    | 0.514 | 0.475 | 0.554 | 0.736 | 0.024 |
+| PA    | 0.514 | 0.475 | 0.553 | 0.745 | 0.024 |
+| CO    | 0.516 | 0.476 | 0.555 | 0.774 | 0.023 |
+| OH    | 0.484 | 0.445 | 0.523 | 0.217 | 0.023 |
+| MI    | 0.518 | 0.479 | 0.558 | 0.816 | 0.024 |
+| WI    | 0.521 | 0.483 | 0.561 | 0.844 | 0.024 |
+| IA    | 0.478 | 0.439 | 0.517 | 0.148 | 0.023 |
+| VA    | 0.523 | 0.482 | 0.562 | 0.863 | 0.023 |
+| MN    | 0.527 | 0.489 | 0.568 | 0.909 | 0.024 |
+| AZ    | 0.471 | 0.430 | 0.510 | 0.077 | 0.024 |
+| GA    | 0.470 | 0.430 | 0.508 | 0.069 | 0.023 |
+| NM    | 0.532 | 0.489 | 0.575 | 0.926 | 0.025 |
+| ME    | 0.542 | 0.503 | 0.582 | 0.985 | 0.024 |
 | SC    | 0.452 | 0.411 | 0.493 | 0.011 | 0.024 |
-| OR    | 0.549 | 0.509 | 0.589 | 0.990 | 0.024 |
-| TX    | 0.443 | 0.403 | 0.485 | 0.002 | 0.025 |
-| MO    | 0.440 | 0.402 | 0.480 | 0.001 | 0.024 |
-| MS    | 0.437 | 0.396 | 0.478 | 0.001 | 0.025 |
-| CT    | 0.565 | 0.524 | 0.606 | 0.999 | 0.025 |
-| WA    | 0.568 | 0.528 | 0.608 | 1.000 | 0.024 |
-| DE    | 0.569 | 0.527 | 0.612 | 1.000 | 0.026 |
+| OR    | 0.549 | 0.509 | 0.590 | 0.992 | 0.025 |
+| TX    | 0.443 | 0.403 | 0.483 | 0.002 | 0.024 |
+| MO    | 0.439 | 0.401 | 0.478 | 0.001 | 0.023 |
+| MS    | 0.436 | 0.397 | 0.476 | 0.001 | 0.024 |
+| CT    | 0.565 | 0.525 | 0.607 | 1.000 | 0.024 |
+| WA    | 0.567 | 0.528 | 0.607 | 1.000 | 0.024 |
+| DE    | 0.568 | 0.525 | 0.609 | 1.000 | 0.025 |
 | AK    | 0.425 | 0.384 | 0.468 | 0.000 | 0.026 |
-| NJ    | 0.578 | 0.538 | 0.619 | 1.000 | 0.025 |
-| IN    | 0.419 | 0.379 | 0.458 | 0.000 | 0.023 |
-| IL    | 0.584 | 0.543 | 0.625 | 1.000 | 0.025 |
-| LA    | 0.411 | 0.373 | 0.451 | 0.000 | 0.024 |
-| MT    | 0.406 | 0.367 | 0.445 | 0.000 | 0.023 |
-| RI    | 0.596 | 0.553 | 0.637 | 1.000 | 0.025 |
-| TN    | 0.404 | 0.365 | 0.443 | 0.000 | 0.023 |
-| KS    | 0.404 | 0.365 | 0.442 | 0.000 | 0.023 |
-| SD    | 0.399 | 0.360 | 0.437 | 0.000 | 0.023 |
-| ND    | 0.390 | 0.351 | 0.429 | 0.000 | 0.023 |
-| NY    | 0.611 | 0.570 | 0.651 | 1.000 | 0.024 |
-| NE    | 0.388 | 0.351 | 0.427 | 0.000 | 0.023 |
-| AL    | 0.385 | 0.346 | 0.425 | 0.000 | 0.024 |
-| AR    | 0.382 | 0.344 | 0.423 | 0.000 | 0.024 |
-| CA    | 0.621 | 0.582 | 0.658 | 1.000 | 0.022 |
-| UT    | 0.375 | 0.338 | 0.414 | 0.000 | 0.023 |
-| KY    | 0.374 | 0.337 | 0.412 | 0.000 | 0.023 |
-| MA    | 0.629 | 0.590 | 0.668 | 1.000 | 0.023 |
-| MD    | 0.639 | 0.598 | 0.681 | 1.000 | 0.025 |
-| WV    | 0.353 | 0.316 | 0.391 | 0.000 | 0.023 |
-| ID    | 0.349 | 0.312 | 0.386 | 0.000 | 0.023 |
-| OK    | 0.342 | 0.305 | 0.381 | 0.000 | 0.023 |
-| VT    | 0.659 | 0.618 | 0.696 | 1.000 | 0.022 |
-| HI    | 0.662 | 0.622 | 0.700 | 1.000 | 0.023 |
+| NJ    | 0.578 | 0.537 | 0.619 | 1.000 | 0.024 |
+| IN    | 0.419 | 0.380 | 0.458 | 0.000 | 0.023 |
+| IL    | 0.583 | 0.543 | 0.623 | 0.999 | 0.024 |
+| LA    | 0.410 | 0.371 | 0.449 | 0.000 | 0.024 |
+| MT    | 0.406 | 0.368 | 0.446 | 0.000 | 0.024 |
+| RI    | 0.595 | 0.555 | 0.636 | 1.000 | 0.024 |
+| KS    | 0.403 | 0.368 | 0.441 | 0.000 | 0.022 |
+| TN    | 0.403 | 0.365 | 0.442 | 0.000 | 0.023 |
+| SD    | 0.398 | 0.361 | 0.438 | 0.000 | 0.023 |
+| NY    | 0.611 | 0.571 | 0.649 | 1.000 | 0.023 |
+| ND    | 0.389 | 0.352 | 0.429 | 0.000 | 0.024 |
+| NE    | 0.388 | 0.351 | 0.428 | 0.000 | 0.023 |
+| AL    | 0.384 | 0.346 | 0.423 | 0.000 | 0.023 |
+| AR    | 0.382 | 0.345 | 0.421 | 0.000 | 0.023 |
+| CA    | 0.620 | 0.581 | 0.658 | 1.000 | 0.023 |
+| UT    | 0.375 | 0.337 | 0.412 | 0.000 | 0.022 |
+| KY    | 0.373 | 0.336 | 0.411 | 0.000 | 0.023 |
+| MA    | 0.629 | 0.591 | 0.668 | 1.000 | 0.023 |
+| MD    | 0.639 | 0.598 | 0.677 | 1.000 | 0.023 |
+| WV    | 0.353 | 0.316 | 0.390 | 0.000 | 0.022 |
+| ID    | 0.349 | 0.313 | 0.386 | 0.000 | 0.022 |
+| OK    | 0.342 | 0.305 | 0.378 | 0.000 | 0.022 |
+| VT    | 0.658 | 0.619 | 0.696 | 1.000 | 0.023 |
+| HI    | 0.661 | 0.620 | 0.699 | 1.000 | 0.023 |
 | WY    | 0.289 | 0.255 | 0.324 | 0.000 | 0.021 |
-| DC    | 0.908 | 0.886 | 0.928 | 1.000 | 0.012 |
+| DC    | 0.908 | 0.885 | 0.928 | 1.000 | 0.012 |
 
 ## Cumulative charts
 
-### Calibration plot
+### Probability calibration plot
 
 ![](README_files/figure-gfm/unnamed-chunk-25-1.png)<!-- -->
 
+### Confidence interval coverage
+
+![](README_files/figure-gfm/unnamed-chunk-27-1.png)<!-- -->
+
+![](README_files/figure-gfm/unnamed-chunk-28-1.png)<!-- -->
+
 # Licence
 
 This software is published by *[The
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diff --git a/model_summary.Rmd b/model_summary.Rmd
index 06ab8e0..db718af 100644
--- a/model_summary.Rmd
+++ b/model_summary.Rmd
@@ -19,7 +19,8 @@ library(ggrepel)
 library(DT)
 
 # final model version
-# source('scripts/model/final_2016.R');source('scripts/model/final_2012.R');source('scripts/model/final_2008.R');rmarkdown::render('model_summary.Rmd',output_file = 'model_reports/v4_cov_error_rewrite');beepr::beep(2);
+# source('scripts/model/final_2016.R');source('scripts/model/final_2012.R');source('scripts/model/final_2008.R');beepr::beep(2);
+# rmarkdown::render('model_summary.Rmd',output_file = 'model_reports/v4_cov_error_rewrite')
 ```
 
 ## Model performance {.tabset .tabset-fade}
diff --git a/scripts/model/final_2008.R b/scripts/model/final_2008.R
index 7ed89fe..f20acf2 100644
--- a/scripts/model/final_2008.R
+++ b/scripts/model/final_2008.R
@@ -64,7 +64,7 @@ check_cov_matrix <- function(mat,wt=state_weights){
 
 ## Master variables
 RUN_DATE <- ymd("2008-11-03")
-#RUN_DATE <- ymd("2008-08-04")
+#RUN_DATE <- ymd("2008-08-11")
 
 election_day <- ymd("2008-11-03")
 start_date <- as.Date("2008-03-01") # Keeping all polls after March 1, 2008
@@ -433,7 +433,7 @@ names(mu_b_prior) == names(prior_diff_score) # correct order?
 
 national_mu_prior <- weighted.mean(inv.logit(mu_b_prior), state_weights)
 
-cat(sprintf('Prior Clinton two-party vote is %s\nWith a national sd of %s\n', 
+cat(sprintf('Prior Obama two-party vote is %s\nWith a national sd of %s\n', 
             round(national_mu_prior,3),round(mu_b_T_scale,3)))
 
 # The model uses national polls to complement state polls when estimating the national term mu_a.
diff --git a/scripts/model/final_2012.R b/scripts/model/final_2012.R
index ca0a77f..b13ba36 100644
--- a/scripts/model/final_2012.R
+++ b/scripts/model/final_2012.R
@@ -64,7 +64,7 @@ check_cov_matrix <- function(mat,wt=state_weights){
 
 ## Master variables
 RUN_DATE <- ymd("2012-11-06")
-#RUN_DATE <- ymd("2012-08-04")
+#RUN_DATE <- ymd("2012-08-11")
 
 election_day <- ymd("2012-11-06")
 start_date <- as.Date("2012-03-01") # Keeping all polls after March 1, 2012
diff --git a/scripts/model/final_2016.R b/scripts/model/final_2016.R
index 66f680d..e574642 100644
--- a/scripts/model/final_2016.R
+++ b/scripts/model/final_2016.R
@@ -64,7 +64,7 @@ check_cov_matrix <- function(mat,wt=state_weights){
 
 ## Master variables
 RUN_DATE <- ymd("2016-11-08")
-#RUN_DATE <- ymd("2016-08-04")
+#RUN_DATE <- ymd("2016-10-19")
 
 election_day <- ymd("2016-11-08")
 start_date <- as.Date("2016-03-01") # Keeping all polls after March 1, 2016
@@ -766,6 +766,25 @@ ex_states <- c('IA','FL','OH','WI','MI','PA','AZ','NC','NH','NV','GA','MN')
 pct_clinton %>% filter(t == RUN_DATE,state %in% c(ex_states,'--')) %>% mutate(se = (high - mean)/1.96) %>% dplyr::select(-t) %>% print
 pct_clinton %>% filter(t == election_day,state %in% c(ex_states,'--')) %>% mutate(se = (high - mean)/1.96) %>% dplyr::select(-t) %>% print
 
+#pct_clinton %>% filter(t == election_day) %>% select(state, clinton=mean) %>% write_csv('~/Desktop/today_2016.csv')
+
+mu_b_t_results_plt <- rbind(mu_b_T_prior_draws, mu_b_T_posterior_draws) %>% 
+  bind_rows(
+    politicaldata::pres_results %>% filter(year == 2016) %>%
+      mutate(mean = dem/(dem+rep)) %>%
+      select(state,mean) %>%
+      mutate(type = 'actual')
+  ) %>%
+  arrange(mean) %>%
+  filter(state != 'DC') %>%
+  ggplot(.) +
+  geom_point(aes(y = mean, x = reorder(state, mean), color = type), position = position_dodge(width = 0.5)) +
+  geom_errorbar(aes(ymin = low, ymax = high, x = state, color = type), width = 0, position = position_dodge(width = 0.5)) +
+  coord_flip() +
+  theme_bw()
+mu_b_t_results_plt
+
+
 map.gg <- urbnmapr::states %>%
   left_join(pct_clinton %>% filter(t == max(t)) %>%
               select(state_abbv=state,prob)) %>%
diff --git a/scripts/model/poll_model_2020 b/scripts/model/poll_model_2020
index 5bec80b..9c754f2 100755
Binary files a/scripts/model/poll_model_2020 and b/scripts/model/poll_model_2020 differ
diff --git a/scripts/model/poll_model_2020.stan b/scripts/model/poll_model_2020.stan
index 8d16eb7..4cb9c1a 100644
--- a/scripts/model/poll_model_2020.stan
+++ b/scripts/model/poll_model_2020.stan
@@ -65,7 +65,7 @@ parameters {
   vector[N_national_polls] raw_measure_noise_national;
   vector[N_state_polls] raw_measure_noise_state;
   vector[S] raw_polling_bias; 
-  real mu_b_T_model_estimation_error;
+  // real mu_b_T_model_estimation_error;
 }
 transformed parameters {
   //*** parameters
@@ -114,7 +114,7 @@ transformed parameters {
 
 model {
   //*** priors
-  raw_mu_b_T ~ std_normal();
+  raw_mu_b_T ~ std_normal(); // student_t(4,0,1); // std_normal();
   //mu_b_T_model_estimation_error ~ scaled_inv_chi_square(7, 1);
   to_vector(raw_mu_b) ~ std_normal();
   raw_mu_c ~ std_normal();
@@ -125,7 +125,7 @@ model {
   raw_e_bias ~ std_normal();
   raw_measure_noise_national ~ std_normal();
   raw_measure_noise_state ~ std_normal();
-  raw_polling_bias ~ std_normal();
+  raw_polling_bias ~  std_normal(); // student_t(4,0,1); // std_normal();
   //*** likelihood
   n_democrat_state ~ binomial_logit(n_two_share_state, logit_pi_democrat_state);
   n_democrat_national ~ binomial_logit(n_two_share_national, logit_pi_democrat_national);
diff --git a/scripts/model/poll_model_2020_no_mode_adjustment b/scripts/model/poll_model_2020_no_mode_adjustment
index 3f00934..3d89e18 100755
Binary files a/scripts/model/poll_model_2020_no_mode_adjustment and b/scripts/model/poll_model_2020_no_mode_adjustment differ
diff --git a/scripts/model/poll_model_2020_no_mode_adjustment.stan b/scripts/model/poll_model_2020_no_mode_adjustment.stan
index 7a8c6a5..31d7230 100644
--- a/scripts/model/poll_model_2020_no_mode_adjustment.stan
+++ b/scripts/model/poll_model_2020_no_mode_adjustment.stan
@@ -65,7 +65,7 @@ parameters {
   vector[N_national_polls] raw_measure_noise_national;
   vector[N_state_polls] raw_measure_noise_state;
   vector[S] raw_polling_bias; 
-  real mu_b_T_model_estimation_error;
+  // real mu_b_T_model_estimation_error;
 }
 transformed parameters {
   //*** parameters
@@ -114,7 +114,7 @@ transformed parameters {
 
 model {
   //*** priors
-  raw_mu_b_T ~ std_normal();
+  raw_mu_b_T ~  std_normal(); // student_t(4,0,1); // std_normal();
   //mu_b_T_model_estimation_error ~ scaled_inv_chi_square(7, 1);
   to_vector(raw_mu_b) ~ std_normal();
   raw_mu_c ~ std_normal();
@@ -125,7 +125,7 @@ model {
   //raw_e_bias ~ std_normal();
   raw_measure_noise_national ~ std_normal();
   raw_measure_noise_state ~ std_normal();
-  raw_polling_bias ~ std_normal();
+  raw_polling_bias ~  std_normal(); // student_t(4,0,1); // std_normal();
   //*** likelihood
   n_democrat_state ~ binomial_logit(n_two_share_state, logit_pi_democrat_state);
   n_democrat_national ~ binomial_logit(n_two_share_national, logit_pi_democrat_national);