- estimation of posterior probability distributions using a stochastic
process called *Markov chain Monte Carlo (MCMC)** estimation
- sample directly from the posterior instead of approximating curves (like the quadratic approximation)
- allows for models that do not assume a multivariate normality
- can use generalized linear and multilevel models
- use Stan to fit these models
- a tale of the Good King Markov
- he was kings of a ring of 10 islands
- the second island had twice the population of the first, the third had three times the population of the first, etc.
- the king wanted to visit the islands in proportion to the population size, but didn’t want to have to keep track of a schedule
- he would also only travel between adjacent islands
- he used the Metropolis algorithm to decide which island to
visit next
- the king decides to stay or travel by a coin flip
- if the coin is heads, the king considers moving clockwise; if tails, he considers moving counter-clockwise; call this next island the “proposal” island
- to decide whether not to move, the king collects the number of seashells in proportion to the population size of the proposed island, and collects the number of stones relative to the population of the current island
- if there are more seashells, the king moves to the proposed island; else he discards the number of stones equal to the number of seashells and randomly selects from the remaining seashells and stones; if he selects a shell, he travels to the proposed island, else he stays
- below is a simulation of this process
set.seed(0)
current_island <- 10
num_weeks <- 1e5
visited_islands <- rep(0, num_weeks)
flip_coin_to_decide_proposal_island <- function(x) {
a <- sample(c(0, 1), 1)
if (a == 0) {
y <- x - 1
} else {
y <- x + 1
}
if (y == 0) {
y <- 10
} else if (y == 11) {
y <- 1
}
return(y)
}
for (wk in seq(1, num_weeks)) {
visited_islands[[wk]] <- current_island
proposal_island <- flip_coin_to_decide_proposal_island(current_island)
if (proposal_island > current_island) {
current_island <- proposal_island
} else {
shells <- rep("shell", proposal_island)
stones <- rep("stone", current_island - proposal_island)
selection <- sample(unlist(c(shells, stones)), 1)
if (selection == "shell") {
current_island <- proposal_island
}
}
}tibble(wk = seq(1, num_weeks),
islands = visited_islands) %>%
ggplot(aes(x = factor(islands))) +
geom_bar(fill = "skyblue4") +
labs(x = "islands",
y = "number of weeks visited",
title = "Distribution of King Markov's visits")
tibble(wk = seq(1, num_weeks),
islands = visited_islands) %>%
slice(1:400) %>%
ggplot(aes(x = wk, y = islands)) +
geom_line(color = "skyblue4", alpha = 0.5) +
geom_point(color = "skyblue4", size = 0.7) +
scale_y_continuous(breaks = c(1:10)) +
labs(x = "week number", y = "island",
title = "The path of King Markov",
subtitle = "Showing only the first 400 weeks")
- this algorithm still works if the king is equally likely to propose
a move to any island from the current island
- still use the proportion of the islands’ populations as the probability of moving
- at any point, the king only needs to know the population of the current island and the proposal island
- the Metropolis algorithm used above is an example of a Markov
chain Monte Carlo
- the goal is to draw samples from an unknown and complex target
distribution
- “islands”: the parameter values (they can be continuous, too)
- “population sizes”: the posterior probabilities at each parameter value
- “week”: samples taken from the joint posterior of the parameters
- we can use the samples from the Metropolis algorithm just like any other sampled distributions so far
- the goal is to draw samples from an unknown and complex target
distribution
- we will also cover Gibbs sampling and Hamiltonian Monte Carlo
- can achieve a more efficient sampling procedure
- adaptive proposals: the distribution of proposed parameter values adjusts itself intelligently depending upon the parameter values at the moment
- the adaptive proposals depend on using particular combinations
of prior distributions and likelihoods known as conjugate pairs
- these pairs have analytical solutions for the posterior of an individual parameter
- can use these solutions to make smart jumps around the joint posterior
- Gibbs sampling is used in BUGS (Bayesian inference Using Gibbs Sampling) and JAGS (Just Another Gibbs Sampler)
- limitations:
- don’t always want to use the conjugate priors
- Gibbs sampling becomes very inefficient for models with hundreds or thousands of parameters
- more computationally expensive than Gibbs sampling and the
Metropolis algorithm, but more efficient
- doesn’t require as many samples to describe the posterior distribution
- an analogy using King Monty:
- king of a continuous stretch of land, not discrete islands
- wants to visit his citizens in proportion to their local density
- decides to travel back and forth across the length of the country, slowing down the vehicle when houses grow more dense, and speeding up when the houses are more sparse
- requires knowledge of how quickly the population is changing at the current location
- in this analogy:
- “royal vehicle”: the current vector of parameter values
- the log-posterior forms a bowl with the MAP at the nadir
- the vehicle sweeps across the bowl, adjust the speed in proportion to the height on the bowl
- HMC does run a sort of physics simulation
- a vector of parameters gives the position of a friction-less particle
- the log-posterior provides the surface
- limitations:
- HMC requires continuous parameters
- HMC needs to be tuned to a particular model and its data
- the particle needs mass (so it can have momentum)
- other hyperparameters of MCMC is handled by Stan
map2stan()provides an interface to Stan similar to how we have usedquap()- need to preprocess and variables transformations
- input data frame can only have columns used in the formulae
- example: look at terrain ruggedness data
data("rugged")
dd <- as_tibble(rugged) %>%
mutate(log_gdp = log(rgdppc_2000)) %>%
filter(!is.na(rgdppc_2000))- fit a model to predict log-GDP with terrain ruggedness, continent, and the interaction of the two
- first fit with
quap()like before
m8_1 <- quap(
alist(
log_gdp ~ dnorm(mu, sigma),
mu <- a + bR*rugged + bA*cont_africa + bAR*rugged*cont_africa,
a ~ dnorm(0, 100),
bR ~ dnorm(0, 10),
bA ~ dnorm(0, 10),
bAR ~ dnorm(0, 10),
sigma ~ dunif(0, 10)
),
data = dd
)
precis(m8_1)#> mean sd 5.5% 94.5%
#> a 9.2227717 0.13798197 9.0022499 9.44329359
#> bR -0.2026506 0.07646932 -0.3248634 -0.08043786
#> bA -1.9469424 0.22450135 -2.3057389 -1.58814589
#> bAR 0.3929006 0.13004832 0.1850583 0.60074296
#> sigma 0.9326829 0.05058184 0.8518433 1.01352241
- must:
- do any transformations beforehand (e.g. logarithm, squared, etc.)
- reduce data frame to only used variables
dd_trim <- dd %>%
select(log_gdp, rugged, cont_africa)- can now fit the model with HMC using
map2stan()
stash("m8_1stan", depends_on = "dd_trim", {
m8_1stan <- map2stan(
alist(
log_gdp ~ dnorm(mu, sigma),
mu <- a + bR*rugged + bA*cont_africa + bAR*rugged*cont_africa,
a ~ dnorm(0, 100),
bR ~ dnorm(0, 10),
bA ~ dnorm(0, 10),
bAR ~ dnorm(0, 10),
sigma ~ dcauchy(0, 2)
),
data = dd_trim
)
})#> Loading stashed object.
precis(m8_1stan)#> mean sd 5.5% 94.5% n_eff Rhat4
#> a 9.2279802 0.13623705 9.0088339 9.43951036 533.1568 0.9996015
#> bR -0.2073055 0.07649824 -0.3246416 -0.08044154 488.1808 0.9990947
#> bA -1.9543006 0.23002089 -2.3383725 -1.57899659 620.7081 0.9990050
#> bAR 0.3981646 0.13082165 0.1962853 0.60559040 534.1085 0.9989998
#> sigma 0.9483330 0.05010279 0.8736709 1.02811206 557.1305 1.0012550
- a half-Cauchy prior was used for (\sigma)
- a uniform distribution would work here, too
- it is a useful, “thick-tailed” probability
- related to the Student (t) distribution
- can think of it as a weakly-regularizing prior for the standard deviation
- the estimates from this model are similar to those from the quadratic prior
- a few differences to note about the output of
precis()(summary()):- the highest probability density intervals (HPDI) are shown, not just percentiles intervals (PI), like before
- two new columns (they will be discussed further later):
n_eff: a crude estimate of the number of independent samples that were collectedRhat4: an estimate of the convergence of the Markov chains (1 is good)
- specific advice on the number of samples to run will be given later
- a compiled model can be resampled from again
- multiple chains can also be used and run in parallel
stash("m8_1stan_4chains", {
m8_1stan_4chains <- map2stan(m8_1stan, chains = 4, cores = 4)
})#> Loading stashed object.
precis(m8_1stan_4chains)#> mean sd 5.5% 94.5% n_eff Rhat4
#> a 9.2208308 0.14563430 8.9854237 9.45860657 1754.883 1.001898
#> bR -0.2016824 0.08038236 -0.3308949 -0.07520045 1696.929 1.000701
#> bA -1.9430034 0.23543180 -2.3190455 -1.56646767 1804.943 1.002227
#> bAR 0.3887506 0.13646037 0.1660543 0.60290154 1705.157 1.003037
#> sigma 0.9510894 0.05287941 0.8711512 1.04114772 2878.768 1.000296
- can plot the samples
- interesting to see how Gaussian the distribution actually was
post <- extract.samples(m8_1stan)
str(post)#> List of 5
#> $ a : num [1:1000(1d)] 9.26 9.46 9.3 9.07 9.37 ...
#> $ bR : num [1:1000(1d)] -0.236 -0.385 -0.207 -0.18 -0.225 ...
#> $ bA : num [1:1000(1d)] -1.92 -2.38 -1.95 -2.12 -2.04 ...
#> $ bAR : num [1:1000(1d)] 0.449 0.645 0.292 0.435 0.32 ...
#> $ sigma: num [1:1000(1d)] 0.951 0.985 0.888 0.915 0.935 ...
pairs(m8_1stan)
- we can use the samples just like before
- simulate predictions
- compute differences of parameters
- calculate DIC and WAIC,
- etc.
show(m8_1stan)#> map2stan model
#> 1000 samples from 1 chain
#>
#> Sampling durations (seconds):
#> warmup sample total
#> chain:1 0.33 0.34 0.67
#>
#> Formula:
#> log_gdp ~ dnorm(mu, sigma)
#> mu <- a + bR * rugged + bA * cont_africa + bAR * rugged * cont_africa
#> a ~ dnorm(0, 100)
#> bR ~ dnorm(0, 10)
#> bA ~ dnorm(0, 10)
#> bAR ~ dnorm(0, 10)
#> sigma ~ dcauchy(0, 2)
#>
#> WAIC (SE): 469 (14.9)
#> pWAIC: 5.09
WAIC(m8_1stan)#> WAIC lppd penalty std_err
#> 1 469.2187 -229.516 5.093422 14.86534
- the MC is guaranteed to converge eventually
- need to check it actually did in the time we gave it
- use the trace plot to plot the samples in sequential order
- this is the first plot that should be looked at after fitting with MCMC
- grey region is the warm up (“adaptation”) where the chain is learning to more efficiently sample from the posterior
- look for 2 characteristics of a “good” chain:
- stationary: the path staying within the posterior distribution
- the center of each path is relatively stable from start to end
- good mixing: each successive sample within each parameter is
not highly correlated with the sample before it
- this is represented by a rapid zig-zap of the paths
- stationary: the path staying within the posterior distribution
plot(m8_1stan)
- can access the raw Stan code in case we want to make specific
changes not possible through
map2stan()
str_split(m8_1stan@model, "\n")#> [[1]]
#> [1] "//2020-06-28 08:59:43"
#> [2] "data{"
#> [3] " int<lower=1> N;"
#> [4] " real log_gdp[N];"
#> [5] " real rugged[N];"
#> [6] " int cont_africa[N];"
#> [7] "}"
#> [8] "parameters{"
#> [9] " real a;"
#> [10] " real bR;"
#> [11] " real bA;"
#> [12] " real bAR;"
#> [13] " real<lower=0> sigma;"
#> [14] "}"
#> [15] "model{"
#> [16] " vector[N] mu;"
#> [17] " sigma ~ cauchy( 0 , 2 );"
#> [18] " bAR ~ normal( 0 , 10 );"
#> [19] " bA ~ normal( 0 , 10 );"
#> [20] " bR ~ normal( 0 , 10 );"
#> [21] " a ~ normal( 0 , 100 );"
#> [22] " for ( i in 1:N ) {"
#> [23] " mu[i] = a + bR * rugged[i] + bA * cont_africa[i] + bAR * rugged[i] * cont_africa[i];"
#> [24] " }"
#> [25] " log_gdp ~ normal( mu , sigma );"
#> [26] "}"
#> [27] "generated quantities{"
#> [28] " vector[N] mu;"
#> [29] " for ( i in 1:N ) {"
#> [30] " mu[i] = a + bR * rugged[i] + bA * cont_africa[i] + bAR * rugged[i] * cont_africa[i];"
#> [31] " }"
#> [32] "}"
#> [33] ""
#> [34] ""
- it is not necessary to fully understand the MCMC, but some understanding of the process is necessary to be able to check if it worked
- defaults:
iter = 2000andwarmup = iter/2- gives 1000 warm-ups and 1000 samples
- this is a good place to start to make sure the model is defined correctly
- the number of samples needed for inference depends on many factors:
- the effective number of samples is the important part
- is an estimate of the number of independent samples from the posterior distribution
- chains can become autocorrelated
- what do we want to know?
- if we just want posterior means, not many samples are needed
- if we care about the shape of the posterior tails/extreme values, then need many samples
- Gaussian posterior distributions should need about 2000 samples, but skewed distributions likely need more
- the effective number of samples is the important part
- for warm-up, we want as few as possible so more time is spent on
sampling, but more warm-ups helps the MCMC sample more efficiently
- for Stan models, it is good to devote half of the total samples to warm-up
- for the very simple models we have fit so far, don’t need very much warm-up
- general workflow
- when debugging a model, use one chain
- run multiple chains to make sure the chains are working
- for final model (whose samples will be use for inference), only one chain is really needed, but running multiple in parallel can help speed it up
- for typical regression models: “four short chains to check, one long chain for inference”
- when sampling is not working right, it is usually very obvious
- we will see some bad chains in the sections below
- Rhat tells us if the chains converged
- use it as a diagnostic signal of danger (when it is above 1.00), but not as a sign of safety (when it is 1.00)
- a common problem is the model has a broad, flat posterior density
- happens most often with flat priors
- this causes the Markov chain to wander erratically
- a simple example with flat priors:
y <- c(-1, 1)
stash("m8_2", depends_on = "y", {
m8_2 <- map2stan(
alist(
y ~ dnorm(mu, sigma),
mu <- alpha
),
data = list(y = y),
start = list(alpha = 0, sigma = 1),
chains = 2,
iter = 4000,
warmup = 1000
)
})#> Loading stashed object.
precis(m8_2)#> mean sd 5.5% 94.5% n_eff Rhat4
#> alpha 340932479 676743337 -1447269.4408 1944065744 3.843847 1.845591
#> sigma 2478059834 21092479052 979.8273 6291182718 123.015633 1.017844
plot(m8_2)
- even weakly informative priors can fix this problem
- the above example just has such little data with two y values and uninformative priors
- with little data, the priors become more important
- here’s the same model with weak, but more informative priors
stash("m8_3", depends_on = "y", {
m8_3 <- map2stan(
alist(
y ~ dnorm(mu, sigma),
mu <- alpha,
alpha ~ dnorm(1, 10),
sigma ~ dcauchy(0, 1)
),
data = list(y = y),
start = list(alpha = 0, sigma = 1),
chains = 2,
iter = 4e3,
warmup = 1e3
)
})#> Loading stashed object.
precis(m8_3)#> mean sd 5.5% 94.5% n_eff Rhat4
#> alpha 0.08573212 1.993184 -2.1229459 2.659450 789.2122 1.00402
#> sigma 2.11063133 2.155345 0.7041848 5.041159 782.4874 1.00443
plot(m8_3)
- below is a plot of the prior and posterior distributions used above
post <- extract.samples(m8_3, n = 1e4, clean = FALSE)
post_tibble <- tibble(parameter = c("alpha", "sigma"),
value = c(list(post$alpha), list(post$sigma))) %>%
unnest(value) %>%
filter((parameter == "sigma" & value > 0 & value < 10) |
parameter == "alpha")
prior_tibble <- tibble(parameter = c("alpha", "sigma"),
x = rep(list(seq(-20, 20, 0.1)), each = 2),
value = c(
list(dnorm(seq(-20, 20, 0.1), 1, 10)),
list(dcauchy(seq(-20, 20, 0.1), 0, 1))
)) %>%
unnest(c(x, value)) %>%
filter((parameter == "sigma" & x > 0 & x < 10) | parameter == "alpha")
post_tibble %>%
ggplot() +
facet_wrap(~ parameter, scales = "free", nrow = 1) +
geom_density(aes(x = value), color = "dodgerblue3", size = 1.3) +
geom_line(data = prior_tibble, aes(x = x, y = value),
color = "darkorange1", size = 1.3, lty = 2) +
labs(x = "values",
y = "probability density",
title = "Priors and posteriors in a simple model",
subtitle = "For each parameter, the prior is in orange and posterior in blue.")
- see the result of highly correlated predictors on a Markov chain
- produce a characteristic shape in the chain
- as an example, fit the following model without any priors on the (\alpha)’s.
[ y_i \sim \text{Normal}(\mu, \sigma) \ \mu = \alpha_1 + \alpha_2 \ \sigma \sim \text{HalfCauchy}(0, 1) ]
y <- rnorm(100, 0, 1)
stash("m8_4", depends_on = "y", {
m8_4 <- map2stan(
alist(
y ~ dnorm(mu, sigma),
mu <- a1 + a2,
sigma ~ dcauchy(0, 1)
),
data = list(y = y),
start = list(a1 = 0, a2 = 0, sigma = 1),
chains = 2,
iter = 4e3,
warmup = 1e3
)
})#> Updating stash.
#>
#> SAMPLING FOR MODEL 'c93922ce2e5a19632999ef5afa9615eb' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 3.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.34 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 4000 [ 0%] (Warmup)
#> Chain 1: Iteration: 400 / 4000 [ 10%] (Warmup)
#> Chain 1: Iteration: 800 / 4000 [ 20%] (Warmup)
#> Chain 1: Iteration: 1001 / 4000 [ 25%] (Sampling)
#> Chain 1: Iteration: 1400 / 4000 [ 35%] (Sampling)
#> Chain 1: Iteration: 1800 / 4000 [ 45%] (Sampling)
#> Chain 1: Iteration: 2200 / 4000 [ 55%] (Sampling)
#> Chain 1: Iteration: 2600 / 4000 [ 65%] (Sampling)
#> Chain 1: Iteration: 3000 / 4000 [ 75%] (Sampling)
#> Chain 1: Iteration: 3400 / 4000 [ 85%] (Sampling)
#> Chain 1: Iteration: 3800 / 4000 [ 95%] (Sampling)
#> Chain 1: Iteration: 4000 / 4000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 3.43336 seconds (Warm-up)
#> Chain 1: 11.9183 seconds (Sampling)
#> Chain 1: 15.3517 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'c93922ce2e5a19632999ef5afa9615eb' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 6e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 4000 [ 0%] (Warmup)
#> Chain 2: Iteration: 400 / 4000 [ 10%] (Warmup)
#> Chain 2: Iteration: 800 / 4000 [ 20%] (Warmup)
#> Chain 2: Iteration: 1001 / 4000 [ 25%] (Sampling)
#> Chain 2: Iteration: 1400 / 4000 [ 35%] (Sampling)
#> Chain 2: Iteration: 1800 / 4000 [ 45%] (Sampling)
#> Chain 2: Iteration: 2200 / 4000 [ 55%] (Sampling)
#> Chain 2: Iteration: 2600 / 4000 [ 65%] (Sampling)
#> Chain 2: Iteration: 3000 / 4000 [ 75%] (Sampling)
#> Chain 2: Iteration: 3400 / 4000 [ 85%] (Sampling)
#> Chain 2: Iteration: 3800 / 4000 [ 95%] (Sampling)
#> Chain 2: Iteration: 4000 / 4000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 3.75279 seconds (Warm-up)
#> Chain 2: 11.317 seconds (Sampling)
#> Chain 2: 15.0698 seconds (Total)
#> Chain 2:
#> Warning: There were 4894 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: The largest R-hat is 1.89, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> http://mc-stan.org/misc/warnings.html#r-hat
#> 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-ess
#> Computing WAIC
precis(m8_4, digits = 3)#> mean sd 5.5% 94.5% n_eff Rhat4
#> a1 -964.4899479 541.57700570 -1818.8111659 -223.393210 1.265054 2.369022
#> a2 964.5184129 541.57708579 223.3781135 1818.810016 1.265051 2.369030
#> sigma 0.9676124 0.07157624 0.8599235 1.076977 24.046541 1.016680
plot(m8_4)
- the chains wander very far and never converge * the
n_effandRhatvalues are horrible * these samples should not be used - using weak priors helps a lot
stash("m8_5", depends_on = "y", {
m8_5 <- map2stan(
alist(
y ~ dnorm(mu, sigma),
mu <- a1 + a2,
c(a1, a2) ~ dnorm(0, 10),
sigma ~ dcauchy(0, 1)
),
data = list(y = y),
start = list(a1 = 0, a2 = 0, sigma = 1),
chains = 2,
iter = 4e3,
warmup = 1e3
)
})#> Updating stash.
#>
#> SAMPLING FOR MODEL 'cf0d22bd53d28861a38ec9ae005b13e6' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 4000 [ 0%] (Warmup)
#> Chain 1: Iteration: 400 / 4000 [ 10%] (Warmup)
#> Chain 1: Iteration: 800 / 4000 [ 20%] (Warmup)
#> Chain 1: Iteration: 1001 / 4000 [ 25%] (Sampling)
#> Chain 1: Iteration: 1400 / 4000 [ 35%] (Sampling)
#> Chain 1: Iteration: 1800 / 4000 [ 45%] (Sampling)
#> Chain 1: Iteration: 2200 / 4000 [ 55%] (Sampling)
#> Chain 1: Iteration: 2600 / 4000 [ 65%] (Sampling)
#> Chain 1: Iteration: 3000 / 4000 [ 75%] (Sampling)
#> Chain 1: Iteration: 3400 / 4000 [ 85%] (Sampling)
#> Chain 1: Iteration: 3800 / 4000 [ 95%] (Sampling)
#> Chain 1: Iteration: 4000 / 4000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 1.339 seconds (Warm-up)
#> Chain 1: 4.29824 seconds (Sampling)
#> Chain 1: 5.63724 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'cf0d22bd53d28861a38ec9ae005b13e6' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 7e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 4000 [ 0%] (Warmup)
#> Chain 2: Iteration: 400 / 4000 [ 10%] (Warmup)
#> Chain 2: Iteration: 800 / 4000 [ 20%] (Warmup)
#> Chain 2: Iteration: 1001 / 4000 [ 25%] (Sampling)
#> Chain 2: Iteration: 1400 / 4000 [ 35%] (Sampling)
#> Chain 2: Iteration: 1800 / 4000 [ 45%] (Sampling)
#> Chain 2: Iteration: 2200 / 4000 [ 55%] (Sampling)
#> Chain 2: Iteration: 2600 / 4000 [ 65%] (Sampling)
#> Chain 2: Iteration: 3000 / 4000 [ 75%] (Sampling)
#> Chain 2: Iteration: 3400 / 4000 [ 85%] (Sampling)
#> Chain 2: Iteration: 3800 / 4000 [ 95%] (Sampling)
#> Chain 2: Iteration: 4000 / 4000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 1.40992 seconds (Warm-up)
#> Chain 2: 4.5868 seconds (Sampling)
#> Chain 2: 5.99672 seconds (Total)
#> Chain 2:
#> Computing WAIC
precis(m8_5, digits = 3)#> mean sd 5.5% 94.5% n_eff Rhat4
#> a1 0.07448717 7.12488901 -11.4065536 11.737317 1728.953 1.001785
#> a2 -0.04792736 7.12207247 -11.7474133 11.449899 1727.991 1.001795
#> sigma 0.94616936 0.06909494 0.8411724 1.061703 2139.499 1.001511
plot(m8_5)
(I skipped to the problems that seemed interesting.)
8H1. Run the model below and then inspect the posterior distribution and explain what it is accomplishing. Compare the samples for the parameters a and b. Can you explain the different trace plots, using what you know about the Cauchy distribution?
# Code from the question:
stash("mp", {
mp <- map2stan(
alist(
a ~ dnorm(0, 1),
b ~ dcauchy(0, 1)
),
data = list(y = 1),
start = list(a = 0, b = 0),
iter = 1e4,
warmup = 1e2,
WAIC = FALSE
)
})#> Loading stashed object.
###precis(mp, digits = 3)#> mean sd 5.5% 94.5% n_eff Rhat4
#> a -0.01937564 0.9901903 -1.639211 1.551454 6378.180 0.9999028
#> b 0.13195629 16.1875973 -5.871213 6.160236 2842.154 1.0009003
plot(mp)
post <- extract.samples(mp, clean = FALSE)
enframe(post) %>%
unnest(value) %>%
ggplot(aes(x = value, color = name)) +
geom_density(size = 1.3) +
scale_x_continuous(limits = c(-10, 10)) +
scale_color_brewer(palette = "Set1")#> Warning: Removed 666 rows containing non-finite values (stat_density).
The posterior plots basically look like their priors. Parameter b has
fatter tails which are descriptive of the Cauchy compared to a Gaussian.
With only one data point, the priors will be very important in defining
the posterior distributions.