# How to Sample From a Distribution

    library(samplr)
    set.seed(123)

### Choosing a distibution

To sample from a distribution, check the abbreviation, as well as the distribution parameters, in the Supported Distributions Vignette. Henceforth we sample from a Normal distribution with mean 0 and standard deviation 1. So, we need:

1. A starting point: Any point in the sampling space. We’ll choose the mean of our distribution, `0`.,
2. Distribution Name: `"norm"`,
3. Distribution Parameters: A vector with the distribution parameters. In our case, `mean` and `std`, that is: `c(0,1)`,

    start = 0
    name = "norm"
    params = c(0,1)

### Different Sampling Algorithms

Different algorithms are available in the `samplr` package. For our example, we will use Metropolis Hastings. To use a sampler, we will always need the three previously mentioned items. For Metropolis Hastings and MC3, it is convenient to provide the variance of the proposal distribution, so we do that before sampling.

    proposal_variance <- 1 / 2
    MH_samples <- sampler_mh(start, name, params, proposal_variance)

If we plot our results:

    hist(MH_samples[[1]], freq=FALSE, breaks = 20)
    curve(dnorm(x, 0,1), add=TRUE)

Notice that the same approach is used for all other samplers:

    MC3_samples <- sampler_mc3(start, name, params, proposal_variance)

    HMC_samples <- sampler_hmc(start, name, params)

### Mixture Distributions

If we want to sample from a mixture distribution, the following change:

1. Distribution Name: A vector of distribution names, e.g. `c("norm", "norm")`
2. Distribution Parameters: A list of vectors, each containing the distribution parameters.
3. Distribution Weights (optional): A vector with the weights given to each distribution. The sum of weights equals 1.

    name <- c("norm", "norm")
    params <- list(c(-2, 1), c(2, 1))
    weights <- c(.4, .6)

We’ll make a custom density function to plot the curve with our results (this is not necessary for the samplers to work)

    customDensity <- function(x){
      dnorm(x, -2, 1) * 0.4 + dnorm(x, 2, 1) * 0.6
    }

We proceed as we did before:

    # Sample (more iterations as MH struggles to change hills)
    proposal_variance <- 1
    MH_samples <- sampler_mh(start, name, params, proposal_variance, iterations = 2**12, weights = weights)
    # plot
    hist(MH_samples[[1]], freq=FALSE, breaks = 20)
    curve(customDensity(x), add=TRUE)

See the Multivariate Mixtures Vignette for examples on how to mix multivariate distributions, as well as a comparison between the performance of different samplers in a ‘patchy’ environment.