  Modelling 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 that require significant computing time. 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. Three applications are
proposed: a usual theoretical example, namely the inference of the parameters
of g-and-k distributions; 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; and simulations from a Ricker model, representing chaotic
population dynamics. In the two first applications, the results show a drastic
reduction of the computational time needed for the inference phase compared to
the other methods, despite an equivalent accuracy. Even when likelihood-based
methods are applicable, the simplicity and efficiency of flimo make it a
compelling alternative. 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.
