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.
Description
Run a bootstrap particle filter for a one-dimensional Gaussian random walk observed with Gaussian noise; report weighted posterior means and resampled final particles.
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.
When to use Sequential Monte Carlo Gaussian Filter
Use this probability or sampling tool to compute a stated quantity or generate reproducible draws from a precisely parameterized distribution or stochastic model.
- observations
- Required list input.
- particle Count
- Required integer input.
- process Standard Deviation
- Required number input.
- observation Standard Deviation
- Required number input.
- initial Standard Deviation
- Required number input.
- seed
- Required string input.
How Sequential Monte Carlo Gaussian Filter works
Run a bootstrap particle filter for a one-dimensional Gaussian random walk observed with Gaussian noise; report weighted posterior means and resampled final particles. 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
- posterior Means
- The resulting posterior means returned as a list.
- final Particles
- The resulting final particles 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
- Hamiltonian Monte Carlo Standard Normal Sampler
Run one-dimensional HMC with leapfrog integration and Metropolis correction for the standard normal target; report acceptance rate.
- Metropolis-Hastings Normal Sampler
Use a Gaussian random-walk Metropolis-Hastings chain targeting the standard normal density. Report acceptance rate; samples are correlated.
- 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.