Estimation of population growth or decline in genetically monitored populations.

By MARK A. Beaumont. 2003

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Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families (2015, background): “Remark-ably, this property is mostly invariant to growing dimensionality, and HMC here often is superior to random walk methods, which need to decrease their step size at a much faster rate [1, Sec.”
Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms (2012, methodology): “This setting was considered by Beaumont in the seminal paper [9] and various extensions proposed in [4].”
Adaptive Multiple Importance Sampling (2009, methodology): “The second analysis combines the AMIS algorithm and an estimation of the likelihood based on IS for gene genealogies in the same way as Beaumont (2003) embedded an IS computation of the likelihood in a MCMC exploration of the parameter space.”
Bayesian forecasting in economics and finance: A modern review (2022, methodology): “, 1993; Chopin and Papaspiliopoulos, 2020) and pseudo-marginal MCMC (Beaumont, 2003; Andrieu and Roberts, 2009; Andrieu et al., 2011) – discussed briefly in Section 3.”
Bayesian computation: a perspective on the current state, and sampling backwards and forwards (2015, methodology): “Andrieu and Roberts (2009) propose a more general resolution of such problems by designing a Metropolis– Hastings algorithm that replaces the intractable target density π ( ·| x ) with an unbiased estimator, following an idea of Beaumont (2003).”
GENETIC APPROACHES FOR ESTIMATING THE EFFECTIVE SIZE OF POPULATIONS (2005, background): “For a more detailed treatment of many of the estimators, see Beaumont (2003a).”
Statistical inference for stochastic simulation models--theory and application. (2011, methodology): “when the algorithm remains several times at the same parameter value), the estimate must not be recalculated, otherwise the resulting distribution may not be unbiased anymore, even if p(Sobs|/) is unbiased (Beaumont 2003; Andrieu & Roberts 2009).”
Bayesian Synthetic Likelihood (2017, methodology): “MCMC BSL is similar to the grouped independence Metropolis–Hastings (GIMH) algorithm of Beaumont (2003) in that a stochastic estimator replaces an intractable likelihood”
Approximate Bayesian Computation in Evolution and Ecology (2010, methodology): “(2003) and another algorithm that has become known as the ‘pseudo-marginal’ method (Andrieu and Roberts, 2009), introduced in (Beaumont, 2003).”
Particle Markov chain Monte Carlo methods (2010, methodology): “This idea has also been exploited in Andrieu and Roberts (2009) and Beaumont (2003) for example and might be appealing since the difficult problem of sampling from p.θ, x 1:T |y 1:T / is reduced to that of sampling from p.θ|y 1:T /, which is typically defined on a much smaller space.”
Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation (2008, methodology): “A second approach pioneered by Beaumont (2003) consists in combining MCMC exploration of the scenario parameter space with an Importance Sampling (IS) based estimation of the likelihood.”
The pseudo-marginal approach for efficient Monte Carlo computations (2009, methodology): “…and its exact generalizations suggested in this paper, and we also give a comparison with an inexact variants, akin to MCWM. Empirical evidence in [1] and in the present paper in the context of reversible jumps for model selection in generalized linear models suggests that the methodology has…”
Robust Demographic Inference from Genomic and SNP Data (2013, background): “Whereas MCMC algorithms generally assume that the likelihood is computed accurately, it has been shown that MCMC procedure should lead to correct posterior distributions even if the likelihood is approximated, provided that there is no systematic error in its computation [65,66].”
Bayesian inference for irreducible diffusion processes using the pseudo-marginal approach (2011, methodology): “MCWM is discussed and studied in Beaumont (2003) and Andrieu and Roberts (2009).”
A THREE-DIMENSIONAL MAP OF MILKY WAY DUST (2015, background): “It is possible, however, to substitute a mathematically correct, but somewhat more computationally expensive algorithm, pseudo-marginal Markov Chain Monte Carlo, (Beaumont 2003; Andrieu & Roberts 2009), in which a new noisy estimate of a marginalized likelihood term is computed at each Markov chain ”
Pseudo-Marginal Bayesian Inference for Gaussian Processes (2013, methodology): “This work shows that the use of the PM approach leads to remarkable efﬁciency in the sampling of hyper-parameters, thus making the fully Bayesian treatment viable and simple to implement and employ.”