Fit multiple parametric distributions, compute KL divergence, simulate best fit

fit_param_dist(vector, num_bins = 30, verbose = TRUE)

Arguments

vector: Numeric vector of data to fit
num_bins: Number of bins for the empirical histogram
verbose: Logical indicating whether to print results

Value

Function to simulate data from the best-fitting distribution

Examples


set.seed(123)
n <- 1000
vector <- rnorm(n)

start <- proc.time()[3]
simulate_function <- fit_param_dist(vector)
#>  [1] 12.56009975  0.06457125  9.61265383  9.60274199  9.57615179         NaN
#>  [7]         Inf  0.01611113         Inf  0.01283163  0.15734994         Inf
#> [1] "Best distribution based on KL divergence: normal"
end <- proc.time()[3]
print(paste("Time taken:", end - start))
#> [1] "Time taken: 0.239000000000001"
simulated_data <- simulate_function(n)  # Generate 100 samples from the best-fit distribution
par(mfrow = c(1, 2))
hist(vector, main = "Original Data", xlab = "Value", ylab = "Frequency")
hist(simulated_data, main = "Simulated Data", xlab = "Value", ylab = "Frequency")