Calculate the posterior mean
Calculate the posterior mean of an object of class Bolstad. If the
object has a member mean then it will return this value otherwise it
will calculate \int_{-∞}^{+∞}θ f(θ|x).dθ using
linear interpolation to approximate the density function and numerical
integration where θ is the variable for which we want to do
Bayesian inference, and x is the data.
## S3 method for class 'Bolstad' mean(x, ...)
x |
An object of class |
... |
Any other arguments. This parameter is currently ignored but it could be useful in the future to deal with problematic data. |
The posterior mean of the variable of inference given the data.
# The useful of this method is really highlighted when we have a general # continuous prior. In this example we are interested in the posterior mean of # an normal mean. Our prior is triangular over [-3, 3] set.seed(123) x = rnorm(20, -0.5, 1) mu = seq(-3, 3, by = 0.001) mu.prior = rep(0, length(mu)) mu.prior[mu <= 0] = 1 / 3 + mu[mu <= 0] / 9 mu.prior[mu > 0] = 1 / 3 - mu[mu > 0] / 9 results = normgcp(x, 1, density = "user", mu = mu, mu.prior = mu.prior) mean(results)
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