x of type dist
with respect to a function g. That is, E(g(x)).R/dist.R
expectation.dist.RdOptionally, we use the CLT to construct a CI(alpha) for the
estimate of the expectation. That is, we estimate E(g(x)) with
the sample mean and Var(g(x)) with the sigma^2/n, where sigma^2
is the sample variance of g(x) and n is the number of samples.
From these, we construct the CI.
# S3 method for dist
expectation(x, g = function(t) t, ..., control = list())dist object
characteristic function of interest, defaults to identity
additional arguments to pass to g
a list of control parameters: compute_stats - Whether to compute CIs for the expectations, defaults to FALSE n - The number of samples to use for the MC estimate, defaults to 10000 alpha - The significance level for the confidence interval, defaults to 0.05
If compute_stats is FALSE, then the estimate of the expectation,
otherwise a list with the following components:
value - The estimate of the expectation
ci - The confidence intervals for each component of the expectation
n - The number of samples