% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MCIS_helpers.R
\name{MC_IS_system}
\alias{MC_IS_system}
\title{Monte-Carlo Importance Sampling for a system of limit-state functions}
\usage{
MC_IS_system(
  lsf,
  lDistr,
  cov_user = 0.05,
  n_batch,
  n_max,
  use_threads,
  sys_type,
  dataRecord = TRUE,
  beta_l,
  densityType,
  dps = NULL,
  debug.level = 0,
  streams = NULL,
  libPaths_local = .libPaths(),
  seed = NULL,
  backend = "future",
  adaptive_alpha = FALSE,
  alpha_update_rate = 0.1,
  adaptive_batch = FALSE,
  batch_control = list(),
  min_adapt_samples = 10000,
  alpha_min = 0.02,
  stability_mode = c("robust", "fast")
)
}
\arguments{
\item{lsf}{Objective (limit state) function in form \code{function(x) \{ ... \}}.}

\item{lDistr}{List of input variable distributions created from TesiproV base variable objects.}

\item{cov_user}{Target coefficient of variation for convergence criterion.}

\item{n_batch}{Number of samples per batch (recommended > 1000).}

\item{n_max}{Maximum total number of samples before forced stop.}

\item{use_threads}{Number of threads/workers to use for parallel computation.}

\item{sys_type}{Either "parallel" (default) or "serial"}

\item{dataRecord}{Logical; if `TRUE`, all intermediate results are recorded and returned as data frame.}

\item{beta_l}{Optional threshold for reliability index screening.
Limit-state functions with \eqn{\beta > beta_l} may be excluded
from mixture construction in system analysis.}

\item{densityType}{Character; type of sampling density ("origin" keeps original input distributions).}

\item{dps}{Optional vector of design points supplied by user instead of FORM result.}

\item{debug.level}{Integer verbosity level: 0 = silent, 1 = info, 2 = detailed output during calculation.}

\item{streams}{RNG stream object created by [`init_rng_master()`].}

\item{libPaths_local}{Local library paths passed to cluster workers for package availability.}

\item{seed}{Optional integer seed for reproducibility across parallel runs. If `NULL`, a random seed is used.}

\item{backend}{Character; either `"parallel"` or `"future"` backend for parallelization on multiple cores.}

\item{adaptive_alpha}{Logical; if \code{TRUE}, adaptive mixture weights are used
(system case only).}

\item{alpha_update_rate}{Damping factor \eqn{\lambda \in [0,1]} controlling
adaptation speed of mixture weights.}

\item{adaptive_batch}{Logical; if TRUE, the batch size is adapted dynamically
based on the current effective sample size (ESS) and target RSE.}

\item{batch_control}{List controlling adaptive batch behaviour:
\describe{
  \item{n_min}{Minimum batch size}
  \item{n_max}{Maximum batch size}
  \item{K_future}{Desired number of remaining iterations}
}}

\item{min_adapt_samples}{Integer. Minimum total number of MC samples required before adaptive mixtures are updated.
The adaption mechanism is activated only after this threshold has been exceeded to ensure statistically stable estimates.}

\item{alpha_min}{Numeric scalar in \eqn{(0,1)}.
Lower bound for mixture weights \eqn{\alpha_i} during adaptive
updating. This prevents individual components of the importance
sampling density from collapsing to zero and ensures numerical
stability in multimodal system analyses.}

\item{stability_mode}{Character string specifying the numerical
  stabilization strategy used for importance sampling weight
  accumulation.

  \describe{
    \item{"robust"}{
      Default. Uses full log-domain accumulation of weights
      across Monte Carlo iterations. This improves numerical
      stability for high-dimensional systems and very small
      failure probabilities.
    }
    \item{"fast"}{
      Uses shifted exponentiation with batch-level scaling.
      Computationally equivalent under normal engineering
      conditions but may be slightly less stable in extreme
      rare-event scenarios.
    }
  }

  Both modes are mathematically equivalent for typical
  reliability problems. The \code{"robust"} mode is recommended
  for research applications and extreme reliability levels.}
}
\description{
Monte-Carlo Importance Sampling for a system of limit-state functions
}
\keyword{internal}
