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 (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和海量的数据。因此，用于估计模型参数的推断算法的效率受到质疑。其中许多算法基于随机优化过程，其拒绝采样方法浪费了很大一部分计算时间。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，根据一些规定的汇总统计量，最小化数据与某些模拟之间的差异。在这个意义上，它类似于近似贝叶斯计算（ABC）。与ABC一样，它也可以提供参数分布的近似。本文提出了三个应用：一个常见的理论示例，即g-and-k分布参数的推断；一个看似简单实则不然的群体遗传学问题，即Wright-Fisher模型中间序列选择性值的推断；以及来自Ricker模型的模拟，代表混沌种群动态。在前两个应用中，尽管精度相当，但结果显示推断阶段所需的计算时间相比其他方法大幅减少。即使基于似然的方法适用，flimo的简单性和高效性也使其成为极具吸引力的替代方案。Julia和R语言的实现可在https://metabarcoding.org/flimo获取。要运行flimo，用户只需能够根据所选模型模拟数据即可。