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.

翻译：生物学建模必须适应日益复杂且海量的数据。因此，用于估计模型参数的推断算法效率受到质疑。其中许多算法基于随机优化过程，需要大量计算时间。我们引入了固定景观推断方法（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。它应用基于梯度的确定性优化算法来获得参数的点估计，通过最小化数据与某些模拟之间基于预设汇总统计量的差异。在此意义上，该方法与近似贝叶斯计算（ABC）类似。与ABC一样，它也能提供参数分布的近似。本文提出了三个应用案例：一个经典理论示例——g-and-k分布的参数推断；一个看似简单实则复杂的人口遗传学问题——在Wright-Fisher模型中从时间序列推断选择值；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，结果显示出推断阶段计算时间的大幅缩减。即使基于似然的方法可行，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语言的实现代码可在https://metabarcoding.org/flimo获取。使用flimo，用户只需能够根据所选模型模拟数据即可。