Use this probability or sampling tool to compute a stated quantity or generate reproducible draws from a precisely parameterized distribution or stochastic model.

start

Required number input.

step Size

Required number input.

leapfrog Steps

Required integer input.

count

Required integer input.

seed

Required string input.

Run one-dimensional HMC with leapfrog integration and Metropolis correction for the standard normal target; report acceptance rate. The tool evaluates the supplied inputs together and returns the named outputs below; it does not infer omitted operating conditions or change the units shown.^[1]

samples

The resulting samples returned as a list.

acceptance Rate

The resulting acceptance rate returned as a number.

• Results depend on parameterization, support, random source, seed, convergence, burn-in, autocorrelation, proposal quality, normalization, numerical tails, and whether model assumptions match the process. Samples are not exact evidence of convergence.
• Use finite inputs in the displayed units and preserve more precision than the final presentation requires. Independently verify safety-critical, financial, compliance, or production decisions.

Check analytic moments or known test cases, use independent chains and diagnostics for Monte Carlo methods, report seed and effective sample size, and inspect tail behavior.

• Metropolis-Hastings Normal Sampler

  Use a Gaussian random-walk Metropolis-Hastings chain targeting the standard normal density. Report acceptance rate; samples are correlated.

• Sequential Monte Carlo Gaussian Filter

  Run a bootstrap particle filter for a one-dimensional Gaussian random walk observed with Gaussian noise; report weighted posterior means and resampled final particles.

• Finite-State Markov Chain Monte Carlo Sampler

  Sample a finite-state Markov chain from a caller-supplied row-stochastic transition matrix and initial state. This samples the chain, not an arbitrary target distribution.