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 which waste a significant part of the computation time due to their rejection sampling approaches. We introduce the Fixed Landscape Inference MethOd (\textit{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 \textit{flimo} make it a compelling alternative. Implementations in Julia and in R are available on \url{https://metabarcoding.org/flimo}. To run \textit{flimo}, the user must simply be able to simulate data according to the chosen model.

翻译：生物建模必须适应日益复杂和大规模的数据，因此用于估计模型参数的推断算法效率备受质疑。许多此类算法基于随机优化过程，因其拒绝采样方法浪费了大量计算时间。我们提出固定景观推断方法（flimo），这是一种适用于连续状态空间随机模型的无似然推断新方法。该方法应用基于梯度的确定性优化算法获得参数的点估计，通过最小化数据与根据指定汇总统计量进行的模拟之间的差异来实现。在此意义上，该方法与近似贝叶斯计算（ABC）类似。与ABC相同，该方法也可提供参数分布的近似。本文提出三个应用案例：一个常规理论示例（即g-and-k分布的参数推断）；一个看似简单实则不然的群体遗传学问题（即基于Wright-Fisher模型时间序列的选择值推断）；以及代表混沌种群动态的Ricker模型模拟。在前两个应用中，尽管精度相当，但与其他方法相比，本方法显著减少了推断阶段所需的计算时间。即使基于似然的方法适用时，flimo的简洁性和高效性也使其成为极具吸引力的替代方案。Julia和R语言实现可参见https://metabarcoding.org/flimo。用户只需能够根据所选模型模拟数据即可运行flimo。