Slice Sampler for Standard Normal
Sample the standard normal density with univariate stepping-out and shrinkage slice sampling. Width controls exploration, not the target distribution.
Description
Sample the standard normal density with univariate stepping-out and shrinkage slice sampling. Width controls exploration, not the target distribution.
Slice Sampler for Standard Normal: Sample the standard normal density with univariate stepping-out and shrinkage slice sampling. Width controls exploration, not the target distribution.
When to use Slice Sampler for Standard Normal
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.
- width
- Required number input.
- count
- Required integer input.
- seed
- Required string input.
How Slice Sampler for Standard Normal works
Sample the standard normal density with univariate stepping-out and shrinkage slice sampling. Width controls exploration, not the target distribution. 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.
Limitations and assumptions
- 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.
Alternative or Complementary approaches
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.
References
-
Monte Carlo method — Wikipedia contributors
Similar or alternative tools
- 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.
- Hamiltonian Monte Carlo Standard Normal Sampler
Run one-dimensional HMC with leapfrog integration and Metropolis correction for the standard normal target; report acceptance rate.
- Lognormal Distribution Sampler
Exponentiate a normally distributed variate with log-space mean μ and standard deviation σ; μ is not the arithmetic mean of samples.