Skip to main content

Table 1 List of acronyms used

From: A survey of Monte Carlo methods for parameter estimation

ABC Approximate Bayesian computation MC Monte Carlo
ADS Adaptive direction sampling MCMC Markov chain Monte Carlo
AGM-MH Adaptive Gaussian mixture Metropolis-Hastings MH Metropolis-Hastings
AIS Adaptive importance sampling MIS Multiple importance sampling
AISM Adaptive independent sticky metropolis ML Maximum likelihood
AM Adaptive Metropolis MMALA Riemann manifold MALA
AMCMC Adaptive Markov chain Monte Carlo MMSE Minimum mean squared error
AMIS Adaptive multiple importance sampling M-PMC Mixture population Monte Carlo
APIS Adaptive population importance sampling MRF Markov random field
ARS Adaptive rejection sampling MSE Mean squared error
ARMS Adaptive rejection Metropolis sampling MTM Multiple-try Metropolis
CDF Cumulative distribution function NUTS No U-turn sampler
CLT Central Limit Theorem OFDM Orthogonal frequency division multiplexing
DA Data augmentation PDF Probability density function
DM Deterministic mixture PMC Population Monte Carlo
DR Delayed rejection PMH Particle Metropolis-Hastings
FUSS Fast universal self-tuned sampler PWC Piecewise constant
GMS Group Metropolis sampling PWL Piecewise linear
HMC Hamiltonian Monte Carlo RMHMC Riemann manifold HMC
IA2RMS Independent doubly adaptive rejection Metropolis sampling RS Rejection sampling
IID Independent and identically distributed RV Random variable
IS Importance sampling SDE Stochastic differential equation
LAIS Layered adaptive importance sampling SIS Sequential importance sampling
MALA Metropolis adjusted Langevin algorithm SMC Sequential Monte Carlo
MAP Maximum a posteriori WSN Wireless sensor network