The Fixed Landscape Inference MethOd (flimo): an alternative to Approximate Bayesian Computation, faster by several orders of magnitudeModelling in biology must adapt to increasingly complex and massive data. The
efficiency of the inference algorithms used to estimate model parameters is
therefore questioned. Many of these are based on stochastic optimization
processes which waste a significant part of the computation time due to their
rejection sampling approaches. We introduce the Fixed Landscape Inference
MethOd (flimo), a new likelihood-free inference method for continuous
state-space stochastic models. It applies deterministic gradient-based
optimization algorithms to obtain a point estimate of the parameters,
minimizing the difference between the data and some simulations according to
some prescribed summary statistics. In this sense, it is analogous to
Approximate Bayesian Computation (ABC). Like ABC, it can also provide an
approximation of the distribution of the parameters. Two applications are
proposed: a usual theoretical example, namely the inference of the parameters
of g-and-k distributions; and a population genetics problem, not so simple as
it seems, namely the inference of a selective value from time series in a
Wright-Fisher model. The results show a drastic reduction of the computational
time needed for the inference phase compared to ABC methods, despite an
equivalent accuracy. Even when likelihood-based methods are applicable, the
simplicity and efficiency of flimo make it a compelling alternative. The flimo
inference method is suitable to many stochastic models involving large data
sets. Implementations in Julia and in R are available on
https://metabarcoding.org/flimo. To run flimo, the user must simply be able to
simulate data according to the chosen model.
arxiv.org