  1. Bayesian inference is difficult because it often requires time consuming
tuning of samplers. Differential evolution Monte-Carlo (DEMC) is a self-tuning
multi-chain sampling approach which requires minimal input from the operator as
samples are obtained by taking the difference of the current position of
multiple randomly selected chains. However, this can also make DEMC more
computationally intensive than single chain samplers. 2. We provide a
single-chain adaptation of the DEMC algorithm by taking samples according to
the difference in previous states of the chain, rather than the current state
of multiple chains. This minimises computational costs by requiring only one
posterior evaluation per step, while retaining the self-adaptive property of
DEMC. We test the algorithm by sampling a bivariate normal distribution and by
estimating the posterior distribution of parameters of an ODE model fitted to
an artificial prey-predator time series. In both cases we compare the quality
of DEMC generated samples to those obtained by a standard adaptive Markov chain
Monte-Carlo sampler (AMC). 3. In both case studies, DEMC is as accurate as AMC
in estimating posterior distributions, while being an order of magnitude faster
due to simpler computations. DEMC also provides a higher effective samples size
than AMC, and lower initial samples autocorrelations. 4. Its low computational
cost and self-adaptive property make single chain DEMC particularly suitable
for fitting models that are costly to evaluate, such as ODE models. The
simplicity of the algorithm also makes it easy to implement in base R, hence
offering a simple alternative to STAN.

Methodology;Populations and Evolution