Model averaging via stacking of predictive distributions, pseudo-BMA weighting or pseudo-BMA+ weighting with the Bayesian bootstrap. See Yao et al. (2018) and Vehtari, Gelman, and Gabry (2017a,2017b) for background.

loo_model_weights(x, ...)

# S3 method for default
loo_model_weights(x, ..., method = c("stacking",
  "pseudobma"), optim_method = "BFGS", optim_control = list(),
  BB = TRUE, BB_n = 1000, alpha = 1, r_eff_list = NULL,
  cores = getOption("mc.cores", 1))

stacking_weights(lpd_point, optim_method = "BFGS",
  optim_control = list())

pseudobma_weights(lpd_point, BB = TRUE, BB_n = 1000, alpha = 1)



A list of pointwise log-likelihood matrices or "psis_loo" objects (objects returned by loo), one for each model. Each matrix/object should have dimensions \(S\) by \(N\), where \(S\) is the size of the posterior sample (with all chains merged) and \(N\) is the number of data points. If x is a list of log-likelihood matrices then loo is called internally on each matrix. Currently the loo_model_weights function is not implemented to be used with results from K-fold CV, but you can still obtain weights using K-fold CV results by calling the stacking_weights function directly.


Unused, except for the generic to pass arguments to individual methods.


Either "stacking" or "pseudobma", indicating which method to use for obtaining the weights. "stacking" refers to stacking of predictive distributions and "pseudobma" refers to pseudo-BMA+ weighting (or plain pseudo-BMA weighting if BB is FALSE).


The optimization method to use if method="stacking". It can be chosen from "Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN" and "Brent". The default method is "BFGS".


If method="stacking", a list of control parameters for optimization. See constrOptim for details.


Logical used when "method"="pseudobma". If TRUE (the default), the Bayesian bootstrap will be used to adjust the pseudo-BMA weighting, which is called pseudo-BMA+ weighting. It helps regularize the weight away from 0 and 1, so as to reduce the variance.


For pseudo-BMA+ weighting only, the number of samples to use for the Bayesian bootstrap. The default is 1000.


Positive scalar shape parameter in the Dirichlet distribution used for the Bayesian bootstrap. The default is 1, which corresponds to a uniform distribution on the simplex space.


Optionally, a list of relative effective sample size estimates for the likelihood (exp(log_lik)) of each observation in each model. See psis and relative_eff helper function for computing r_eff. If x is a list of "psis_loo" objects then r_eff_list is ignored.


The number of cores to use for parallelization. This defaults to the option mc.cores which can be set for an entire R session by options(mc.cores = NUMBER). The old option loo.cores is now deprecated but will be given precedence over mc.cores until loo.cores is removed in a future release. As of version 2.0.0 the default is now 1 core if mc.cores is not set, but we recommend using as many (or close to as many) cores as possible. Note for Windows 10 users: it is recommended to avoid using the .Rprofile file to set mc.cores (using the cores argument or setting mc.cores interactively or in a script is fine).


A matrix of pointwise leave-one-out (or K-fold) log likelihoods evaluated for different models. It should be a \(N\) by \(K\) matrix where \(N\) is sample size and \(K\) is the number of models. Each column corresponds to one model. These values can be calculated approximately using loo or by running exact leave-one-out or K-fold cross-validation.


A numeric vector containing one weight for each model.


loo_model_weights is a wrapper around the stacking_weights and pseudobma_weights functions that implements stacking, pseudo-BMA, and pseudo-BMA+ weighting for combining multiple predictive distributions. We can use approximate or exact leave-one-out cross-validation (LOO-CV) or K-fold CV to estimate the expected log predictive density (ELPD).

The stacking method (method="stacking") combines all models by maximizing the leave-one-out predictive density of the combination distribution. That is, it finds the optimal linear combining weights for maximizing the leave-one-out log score.

The pseudo-BMA method (method="pseudobma") finds the relative weights proportional to the ELPD of each model. However, when method="pseudobma", the default is to also use the Bayesian bootstrap (BB=TRUE), which corresponds to the pseudo-BMA+ method. The Bayesian bootstrap takes into account the uncertainty of finite data points and regularizes the weights away from the extremes of 0 and 1.

In general, we recommend stacking for averaging predictive distributions, while pseudo-BMA+ can serve as a computationally easier alternative.


Vehtari, A., Gelman, A., and Gabry, J. (2017a). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 27(5), 1413--1432. doi:10.1007/s11222-016-9696-4. ( journal, preprint arXiv:1507.04544).

Vehtari, A., Gelman, A., and Gabry, J. (2017b). Pareto smoothed importance sampling. arXiv preprint:

Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018) Using stacking to average Bayesian predictive distributions. Bayesian Analysis, advance publication, doi:10.1214/17-BA1091. (online).

See also

  • The loo package vignettes for demonstrations.

  • loo for details on leave-one-out ELPD estimation.

  • constrOptim for the choice of optimization methods and control-parameters.

  • relative_eff for computing r_eff.


### Demonstrating usage after fitting models with RStan

# generate fake data from N(0,1).
N <- 100
y <- rnorm(N, 0, 1)

# Suppose we have three models: N(-1, sigma), N(0.5, sigma) and N(0.6,sigma).
stan_code <- "
  data {
    int N;
    vector[N] y;
    real mu_fixed;
  parameters {
    real<lower=0> sigma;
  model {
    sigma ~ exponential(1);
    y ~ normal(mu_fixed, sigma);
  generated quantities {
    vector[N] log_lik;
    for (n in 1:N) log_lik[n] = normal_lpdf(y[n]| mu_fixed, sigma);

mod <- stan_model(model_code = stan_code)
fit1 <- sampling(mod, data=list(N=N, y=y, mu_fixed=-1))
fit2 <- sampling(mod, data=list(N=N, y=y, mu_fixed=0.5))
fit3 <- sampling(mod, data=list(N=N, y=y, mu_fixed=0.6))
model_list <- list(fit1, fit2, fit3)
log_lik_list <- lapply(model_list, extract_log_lik)

# optional but recommended
r_eff_list <- lapply(model_list, function(x) {
  ll_array <- extract_log_lik(x, merge_chains = FALSE)

# stacking method:
wts1 <- loo_model_weights(
  method = "stacking",
  r_eff_list = r_eff_list,
  optim_control = list(reltol=1e-10)

# can also pass a list of psis_loo objects to avoid recomputing loo
loo_list <- lapply(1:length(log_lik_list), function(j) {
  loo(log_lik_list[[j]], r_eff = r_eff_list[[j]])

wts2 <- loo_model_weights(
  method = "stacking",
  optim_control = list(reltol=1e-10)
all.equal(wts1, wts2)

# pseudo-BMA+ method:
loo_model_weights(loo_list, method = "pseudobma")

# pseudo-BMA method (set BB = FALSE):
loo_model_weights(loo_list, method = "pseudobma", BB = FALSE)

# calling stacking_weights or pseudobma_weights directly
lpd1 <- loo(log_lik_list[[1]], r_eff = r_eff_list[[1]])$pointwise[,1]
lpd2 <- loo(log_lik_list[[2]], r_eff = r_eff_list[[2]])$pointwise[,1]
lpd3 <- loo(log_lik_list[[3]], r_eff = r_eff_list[[3]])$pointwise[,1]
stacking_weights(cbind(lpd1, lpd2, lpd3))
pseudobma_weights(cbind(lpd1, lpd2, lpd3))
pseudobma_weights(cbind(lpd1, lpd2, lpd3), BB = FALSE)
# }