compute_mallows_sequentially {BayesMallows}

Estimate the Bayesian Mallows Model Sequentially

Description

Compute the posterior distributions of the parameters of the Bayesian Mallows model using sequential Monte Carlo. This is based on the algorithms developed in Stein (2023). This function differs from update_mallows() in that it takes all the data at once, and uses SMC to fit the model step-by-step. Used in this way, SMC is an alternative to Metropolis-Hastings, which may work better in some settings. In addition, it allows visualization of the learning process.

Usage

compute_mallows_sequentially(
  data,
  initial_values,
  model_options = set_model_options(),
  smc_options = set_smc_options(),
  compute_options = set_compute_options(),
  priors = set_priors(),
  pfun_estimate = NULL
)

Arguments

data

A list of objects of class "BayesMallowsData" returned from setup_rank_data(). Each list element is interpreted as the data belonging to a given timepoint.

initial_values

An object of class "BayesMallowsPriorSamples" returned from sample_prior().

model_options

An object of class "BayesMallowsModelOptions" returned from set_model_options().

smc_options

An object of class "SMCOptions" returned from set_smc_options().

compute_options

An object of class "BayesMallowsComputeOptions" returned from set_compute_options().

priors

An object of class "BayesMallowsPriors" returned from set_priors().

pfun_estimate

Object returned from estimate_partition_function(). Defaults to NULL, and will only be used for footrule, Spearman, or Ulam distances when the cardinalities are not available, cf. get_cardinalities().

Details

This function is very new, and plotting functions and other tools for visualizing the posterior distribution do not yet work. See the examples for some workarounds.

Value

An object of class BayesMallowsSequential.

References

Stein A (2023). Sequential Inference with the Mallows Model. Ph.D. thesis, Lancaster University.

See Also

Other modeling: burnin(), burnin<-(), compute_mallows(), compute_mallows_mixtures(), sample_prior(), update_mallows()

Examples

# Observe one ranking at each of 12 timepoints
library(ggplot2)
data <- lapply(seq_len(nrow(potato_visual)), function(i) {
  setup_rank_data(potato_visual[i, ], user_ids = i)
})

initial_values <- sample_prior(
  n = 200, n_items = 20,
  priors = set_priors(gamma = 3, lambda = .1))

mod <- compute_mallows_sequentially(
  data = data,
  initial_values = initial_values,
  smc_options = set_smc_options(n_particles = 500, mcmc_steps = 20))

# We can see the acceptance ratio of the move step for each timepoint:
get_acceptance_ratios(mod)

plot_dat <- data.frame(
  n_obs = seq_along(data),
  alpha_mean = apply(mod$alpha_samples, 2, mean),
  alpha_sd = apply(mod$alpha_samples, 2, sd)
)

# Visualize how the dispersion parameter is being learned as more data arrive
ggplot(plot_dat, aes(x = n_obs, y = alpha_mean, ymin = alpha_mean - alpha_sd,
                     ymax = alpha_mean + alpha_sd)) +
  geom_line() +
  geom_ribbon(alpha = .1) +
  ylab(expression(alpha)) +
  xlab("Observations") +
  theme_classic() +
  scale_x_continuous(
    breaks = seq(min(plot_dat$n_obs), max(plot_dat$n_obs), by = 1))

# Visualize the learning of the rank for a given item (item 1 in this example)
plot_dat <- data.frame(
  n_obs = seq_along(data),
  rank_mean = apply(mod$rho_samples[1, , ], 2, mean),
  rank_sd = apply(mod$rho_samples[1, , ], 2, sd)
)

ggplot(plot_dat, aes(x = n_obs, y = rank_mean, ymin = rank_mean - rank_sd,
                     ymax = rank_mean + rank_sd)) +
  geom_line() +
  geom_ribbon(alpha = .1) +
  xlab("Observations") +
  ylab(expression(rho[1])) +
  theme_classic() +
  scale_x_continuous(
    breaks = seq(min(plot_dat$n_obs), max(plot_dat$n_obs), by = 1))