Table 1 List of acronyms used

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