Assignment #8
Ruohai Wang
2021-05-17
Chapter 9 - Markov Chain Monte Carlo
This chapter has been an informal introduction to Markov chain Monte Carlo (MCMC) estimation. The goal has been to introduce the purpose and approach MCMC algorithms. The major algorithms introduced were the Metropolis, Gibbs sampling, and Hamiltonian Monte Carlo algorithms. Each has its advantages and disadvantages. The ulam function in the rethinking package was introduced. It uses the Stan (mc-stan.org) Hamiltonian Monte Carlo engine to fit models as they are defined in this book. General advice about diagnosing poor MCMC fits was introduced by the use of a couple of pathological examples.
Place each answer inside the code chunk (grey box). The code chunks should contain a text response or a code that completes/answers the question or activity requested. Make sure to include plots if the question requests them. Problems are labeled Easy (E), Medium (M), and Hard(H).
Finally, upon completion, name your final output .html file as: YourName_ANLY505-Year-Semester.html and publish the assignment to your R Pubs account and submit the link to Canvas. Each question is worth 5 points.
Questions
9E1. Which of the following is a requirement of the simple Metropolis algorithm?
- The parameters must be discrete.
- The likelihood function must be Gaussian.
- The proposal distribution must be symmetric.
#3. The proposal distribution must be symmetric.9E2. Gibbs sampling is more efficient than the Metropolis algorithm. How does it achieve this extra efficiency? Are there any limitations to the Gibbs sampling strategy?
#1. Gibbs sampling is more efficient than the Metropolis algorithm because its distribution of proposed parameter values is adjusted to currect parameter values.
#2. In terms of limitations, Gibbs sampling uses pairs of priors and likelihoods that have analytic solutions for the posterior of an individual parameter. 9E3. Which sort of parameters can Hamiltonian Monte Carlo not handle? Can you explain why?
# Hamiltonian Monte Carlo cannot handle discrete parameters which is based on parameters as speed proportional to the gradient of likelihood, and so discrete parameters aren’t differentiable.9E4. Explain the difference between the effective number of samples, n_eff as calculated by Stan, and the actual number of samples.
# Firstly, the effective number of samples is an estimate of the number of independent samples from the posterior distribution, in terms of estimating some function like the posterior mean.
# Secondly, n_eff is an estimates of effective number of samples, it is used to estimate the posterior mean.
# Thirdly ,the actual number of samples: samples we use for accurate inference.9E5. Which value should Rhat approach, when a chain is sampling the posterior distribution correctly?
# R-hat should be close to 1. It reflects the fact that inner variance and outer variance between chains is about the same. Therefore, the inference is not broken.9E6. Sketch a good trace plot for a Markov chain, one that is effectively sampling from the posterior distribution. What is good about its shape? Then sketch a trace plot for a malfunctioning Markov chain. What about its shape indicates malfunction?
data(rugged)
d <- rugged
d$log_gdp <- log(d$rgdppc_2000)
dd <- d[ complete.cases(d$rgdppc_2000) , ]
dd$log_gdp_std <- dd$log_gdp / mean(dd$log_gdp)
dd$rugged_std <- dd$rugged / max(dd$rugged)
dd$cid <- ifelse( dd$cont_africa == 1 , 1 , 2)
m9.6 <- quap(
alist(
log_gdp_std ~ dnorm(mu, sigma),
mu <- a[cid] + b[cid]*( rugged_std - 0.215 ),
a[cid] ~ dnorm(1, 0.1 ),
b[cid] ~ dnorm(0, 0.3 ),
sigma ~ dexp(1)),
data = dd)
precis(m9.6 , depth = 2)## mean sd 5.5% 94.5%
## a[1] 0.8865701 0.015675198 0.86151815 0.91162214
## a[2] 1.0505623 0.009936284 1.03468223 1.06644243
## b[1] 0.1326706 0.074201985 0.01408154 0.25125975
## b[2] -0.1427182 0.054747499 -0.23021530 -0.05522114
## sigma 0.1094905 0.005934801 0.10000552 0.11897543dat_slim <- list(
log_gdp_std = dd$log_gdp_std,
rugged_std = dd$rugged_std,
cid = as.integer( dd$cid ))
str(dat_slim)## List of 3
## $ log_gdp_std: num [1:170] 0.88 0.965 1.166 1.104 0.915 ...
## $ rugged_std : num [1:170] 0.138 0.553 0.124 0.125 0.433 ...
## $ cid : int [1:170] 1 2 2 2 2 2 2 2 2 1 ...m9.1 <- ulam(
alist(
log_gdp_std ~ dnorm(mu, sigma) ,
mu <- a[cid] + b[cid]*(rugged_std - 0.215) ,
a[cid] ~ dnorm(1, 0.1) ,
b[cid] ~ dnorm(0, 0.3) ,
sigma ~ dexp(1)
) , data=dat_slim , chains=4 , cores=4)## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1show(m9.1)## Hamiltonian Monte Carlo approximation
## 2000 samples from 4 chains
##
## Sampling durations (seconds):
## warmup sample total
## chain:1 0.07 0.05 0.11
## chain:2 0.06 0.04 0.10
## chain:3 0.07 0.04 0.12
## chain:4 0.07 0.04 0.11
##
## Formula:
## log_gdp_std ~ dnorm(mu, sigma)
## mu <- a[cid] + b[cid] * (rugged_std - 0.215)
## a[cid] ~ dnorm(1, 0.1)
## b[cid] ~ dnorm(0, 0.3)
## sigma ~ dexp(1)traceplot(m9.1)## [1] 1000
## [1] 1
## [1] 1000# Based on the trace plot, the samples were plotted in sequential order, joined by a line. We take a look of 3 factors while a chain is healthy: (a stationarity, (b) good mixing, and (c) convergence. If the 3 factors are not met, the Markov chain trace plot is malfunctional.
y <- c(-1,1)
set.seed(11)
m9.2 <- ulam(
alist(
y ~ dnorm(mu, sigma) ,
mu <- alpha ,
alpha ~ dnorm(0 , 1000) ,
sigma ~ dexp(0.0001)
) , data=list(y=y), chains=3)## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1
##
## SAMPLING FOR MODEL '726d002e27cec1633082261fcfedb813' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 1.8e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
## Chain 1: Adjust your expectations accordingly!
## Chain 1:
## Chain 1:
## Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup)
## Chain 1: Iteration: 100 / 1000 [ 10%] (Warmup)
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## Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling)
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## Chain 1: Iteration: 700 / 1000 [ 70%] (Sampling)
## Chain 1: Iteration: 800 / 1000 [ 80%] (Sampling)
## Chain 1: Iteration: 900 / 1000 [ 90%] (Sampling)
## Chain 1: Iteration: 1000 / 1000 [100%] (Sampling)
## Chain 1:
## Chain 1: Elapsed Time: 0.026679 seconds (Warm-up)
## Chain 1: 0.020204 seconds (Sampling)
## Chain 1: 0.046883 seconds (Total)
## Chain 1:
##
## SAMPLING FOR MODEL '726d002e27cec1633082261fcfedb813' NOW (CHAIN 2).
## Chain 2:
## Chain 2: Gradient evaluation took 4e-06 seconds
## Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
## Chain 2: Adjust your expectations accordingly!
## Chain 2:
## Chain 2:
## Chain 2: Iteration: 1 / 1000 [ 0%] (Warmup)
## Chain 2: Iteration: 100 / 1000 [ 10%] (Warmup)
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## Chain 2: Iteration: 501 / 1000 [ 50%] (Sampling)
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## Chain 2: Iteration: 900 / 1000 [ 90%] (Sampling)
## Chain 2: Iteration: 1000 / 1000 [100%] (Sampling)
## Chain 2:
## Chain 2: Elapsed Time: 0.027475 seconds (Warm-up)
## Chain 2: 0.032076 seconds (Sampling)
## Chain 2: 0.059551 seconds (Total)
## Chain 2:
##
## SAMPLING FOR MODEL '726d002e27cec1633082261fcfedb813' NOW (CHAIN 3).
## Chain 3:
## Chain 3: Gradient evaluation took 4e-06 seconds
## Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.
## Chain 3: Adjust your expectations accordingly!
## Chain 3:
## Chain 3:
## Chain 3: Iteration: 1 / 1000 [ 0%] (Warmup)
## Chain 3: Iteration: 100 / 1000 [ 10%] (Warmup)
## Chain 3: Iteration: 200 / 1000 [ 20%] (Warmup)
## Chain 3: Iteration: 300 / 1000 [ 30%] (Warmup)
## Chain 3: Iteration: 400 / 1000 [ 40%] (Warmup)
## Chain 3: Iteration: 500 / 1000 [ 50%] (Warmup)
## Chain 3: Iteration: 501 / 1000 [ 50%] (Sampling)
## Chain 3: Iteration: 600 / 1000 [ 60%] (Sampling)
## Chain 3: Iteration: 700 / 1000 [ 70%] (Sampling)
## Chain 3: Iteration: 800 / 1000 [ 80%] (Sampling)
## Chain 3: Iteration: 900 / 1000 [ 90%] (Sampling)
## Chain 3: Iteration: 1000 / 1000 [100%] (Sampling)
## Chain 3:
## Chain 3: Elapsed Time: 0.029399 seconds (Warm-up)
## Chain 3: 0.040027 seconds (Sampling)
## Chain 3: 0.069426 seconds (Total)
## Chain 3:## Warning: There were 25 divergent transitions after warmup. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## to find out why this is a problem and how to eliminate them.## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## http://mc-stan.org/misc/warnings.html#bulk-ess## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## http://mc-stan.org/misc/warnings.html#tail-essshow(m9.2)## Hamiltonian Monte Carlo approximation
## 1500 samples from 3 chains
##
## Sampling durations (seconds):
## warmup sample total
## chain:1 0.03 0.02 0.05
## chain:2 0.03 0.03 0.06
## chain:3 0.03 0.04 0.07
##
## Formula:
## y ~ dnorm(mu, sigma)
## mu <- alpha
## alpha ~ dnorm(0, 1000)
## sigma ~ dexp(1e-04)traceplot(m9.2)
## [1] 1000
## [1] 1
## [1] 1000
9E7. Repeat the problem above, but now for a trace rank plot.
trankplot(m9.1)
trankplot(m9.2)
9M1. Re-estimate the terrain ruggedness model from the chapter, but now using a uniform prior for the standard deviation, sigma. The uniform prior should be dunif(0,1). Visualize the priors. Use ulam to estimate the posterior. Visualize the posteriors for both models. Does the different prior have any detectible influence on the posterior distribution of sigma? Why or why not?
data(rugged)
d <- rugged
d$log_gdp <- log(d$rgdppc_2000)
dd <- d[ complete.cases(d$rgdppc_2000) , ]
dd$log_gdp_std <- dd$log_gdp/ mean(dd$log_gdp)
dd$rugged_std<- dd$rugged/max(dd$rugged)
dd$cid<-ifelse(dd$cont_africa==1,1,2)
m9.3 <- quap(
alist(
log_gdp_std ~ dnorm( mu , sigma ) ,
mu <- a[cid] + b[cid]* (rugged_std-0.215) ,
a[cid] ~ dnorm(1,0.1),
b[cid] ~ dnorm(0,0.3),
sigma ~ dexp(1)
) ,
data=dd)
precis(m9.3 , depth=2)## mean sd 5.5% 94.5%
## a[1] 0.8865660 0.015675078 0.86151419 0.91161779
## a[2] 1.0505679 0.009936208 1.03468791 1.06644787
## b[1] 0.1325350 0.074201585 0.01394649 0.25112342
## b[2] -0.1425568 0.054747270 -0.23005354 -0.05506012
## sigma 0.1094897 0.005934696 0.10000487 0.11897445pairs(m9.3)
m9.3_unif <- quap(
alist(
log_gdp_std ~ dnorm( mu , sigma ) ,
mu <- a[cid] + b[cid]* (rugged_std-0.215) ,
a[cid] ~ dnorm(1,0.1),
b[cid] ~ dnorm(0,0.3),
sigma ~ dunif(0,1)
) ,
data=dd)
precis(m9.3_unif , depth=2)## mean sd 5.5% 94.5%
## a[1] 0.8865646 0.015680645 0.86150390 0.91162530
## a[2] 1.0505685 0.009939796 1.03468276 1.06645419
## b[1] 0.1325028 0.074227013 0.01387368 0.25113189
## b[2] -0.1425733 0.054766564 -0.23010089 -0.05504579
## sigma 0.1095296 0.005940112 0.10003617 0.11902306pairs(m9.3_unif)
#It does not have detectable influence on the posterior distribution of sigma.9M2. Modify the terrain ruggedness model again. This time, change the prior for b[cid] to dexp(0.3). What does this do to the posterior distribution? Can you explain it?
m9.3_exp <- quap(
alist(
log_gdp_std ~ dnorm(mu , sigma) ,
mu <- a[cid] + b[cid]* (rugged_std-0.215) ,
a[cid] ~ dnorm(1,0.1),
b[cid] ~ dnorm(0,0.3),
sigma ~ dexp(0.3)
) ,
data=dd)
precis(m9.3_exp , depth=2)## mean sd 5.5% 94.5%
## a[1] 0.8865649 0.015679232 0.86150647 0.91162335
## a[2] 1.0505696 0.009938884 1.03468537 1.06645388
## b[1] 0.1325026 0.074220566 0.01388385 0.25112145
## b[2] -0.1425740 0.054761661 -0.23009371 -0.05505429
## sigma 0.1095195 0.005938737 0.10002822 0.11901072pairs(m9.3_exp)
# There seems to be no differences in the posterior distribution.9M3. Re-estimate one of the Stan models from the chapter, but at different numbers of warmup iterations. Be sure to use the same number of sampling iterations in each case. Compare the n_eff values. How much warmup is enough?
m9.4 <- ulam(
alist(
log_gdp_std ~ dnorm(mu , sigma) ,
mu <- a[cid] + b[cid]*( rugged_std - 0.215) ,
a[cid] ~ dnorm(1 , 0.1) ,
b[cid] ~ dnorm(0 , 0.3) ,
sigma ~ dexp(1)
) , data=dat_slim , chains=4 , cores=4)
precis(m9.4, depth=2)## mean sd 5.5% 94.5% n_eff Rhat4
## a[1] 0.8867544 0.015997741 0.86215748 0.91227352 3450.006 0.9989706
## a[2] 1.0505851 0.010190587 1.03446373 1.06672717 2534.798 1.0000621
## b[1] 0.1330407 0.075296395 0.01564941 0.25222800 2595.367 0.9988701
## b[2] -0.1413581 0.056017108 -0.22998829 -0.04893445 2559.966 0.9997827
## sigma 0.1115539 0.005802838 0.10263806 0.12113434 2527.737 0.9998120pairs(m9.1)
#500 warmup is enough.9H1. Run the model below and then inspect the posterior distribution and explain what it is accomplishing.
mp <- ulam(
alist(
a ~ dnorm(0,1),
b ~ dcauchy(0,1)
), data=list(y=1) , chains=1 )## Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
## clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fPIC -Wall -g -O2 -c foo.c -o foo.o
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
## /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name 'namespace'
## namespace Eigen {
## ^
## /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:16: error: expected ';' after top level declarator
## namespace Eigen {
## ^
## ;
## In file included from <built-in>:1:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
## In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
## /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
## #include <complex>
## ^~~~~~~~~
## 3 errors generated.
## make: *** [foo.o] Error 1
##
## SAMPLING FOR MODEL '3bd3f4d287e9cccab124308e5415245c' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 1e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.
## Chain 1: Adjust your expectations accordingly!
## Chain 1:
## Chain 1:
## Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup)
## Chain 1: Iteration: 100 / 1000 [ 10%] (Warmup)
## Chain 1: Iteration: 200 / 1000 [ 20%] (Warmup)
## Chain 1: Iteration: 300 / 1000 [ 30%] (Warmup)
## Chain 1: Iteration: 400 / 1000 [ 40%] (Warmup)
## Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup)
## Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling)
## Chain 1: Iteration: 600 / 1000 [ 60%] (Sampling)
## Chain 1: Iteration: 700 / 1000 [ 70%] (Sampling)
## Chain 1: Iteration: 800 / 1000 [ 80%] (Sampling)
## Chain 1: Iteration: 900 / 1000 [ 90%] (Sampling)
## Chain 1: Iteration: 1000 / 1000 [100%] (Sampling)
## Chain 1:
## Chain 1: Elapsed Time: 0.012313 seconds (Warm-up)
## Chain 1: 0.045278 seconds (Sampling)
## Chain 1: 0.057591 seconds (Total)
## Chain 1:## Warning: There were 5 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See
## http://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded## Warning: Examine the pairs() plot to diagnose sampling problems## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
## http://mc-stan.org/misc/warnings.html#bulk-ess## Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
## Running the chains for more iterations may help. See
## http://mc-stan.org/misc/warnings.html#tail-esstraceplot(mp)## [1] 1000
## [1] 1
## [1] 1000
Compare the samples for the parameters a and b. Can you explain the different trace plots? If you are unfamiliar with the Cauchy distribution, you should look it up. The key feature to attend to is that it has no expected value. Can you connect this fact to the trace plot?