diagis: Auxiliary functions and diagnostic plots for importance...

In helske/diagis: Diagnostic Plot and Multivariate Summary Statistics of Weighted Samples from Importance Sampling

Auxiliary functions and diagnostic plots for importance sampling

Description

This package contains functions computing weighted (running) summaries and diagonostic plots for importance sampling problems.

Examples

# simple importance sampling example
# true distribution is a standard normal:
p <- function(x) dnorm(x)
# proposal distribution is normal with sd s
q <- function(x, s) dnorm(x, 0, s)

# IS weights have finite variance only if s^2 > 1/2
# variance is s/(2-1/s^2)^(3/2)

#optimal case
set.seed(42)
s_opt <- sqrt(2)
x_opt <- rnorm(1000, sd = s_opt)
w_opt <- p(x_opt) / q(x_opt, s_opt) 
weighted_mean(x_opt, w_opt)
weighted_var(x_opt, w_opt)
s_inf <- 0.25
x_inf <- rnorm(1000, sd = s_inf)
w_inf <- p(x_inf) / q(x_inf, s_inf)
weighted_mean(x_inf, w_inf) #!!
weighted_var(x_inf, w_inf) #!!
# diagnostic plots
weight_plot(w_inf)
weight_plot(w_opt)

helske/diagis documentation built on Sept. 9, 2023, 8:15 a.m.