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. 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.
翻译:生物学模型必须适应日益复杂和庞大的数据。 因此,用于估计模型参数的测算算法的效率受到质疑。 其中有许多基于沙沙优化优化过程,这些过程由于拒绝取样方法而浪费了大部分计算时间。 我们采用了固定地貌推断法MethOd(Floimo),这是用于连续的州空间随机模型的一个新的无可能性推断法。它采用基于梯度的确定性优化算法,以获得参数的点估计值,根据某些规定的简要统计数据将数据与某些模拟数据之间的差值最小化。从这个意义上讲,它类似于Apbsear Bayesian Computation(ABC) 。它和ABC一样,也可以提供参数分布的近似近似。我们提出了两种应用:通常的理论范例,即G-和k分布模型参数的误差;以及人口遗传学问题,似乎不那么简单,即时间序列的选择性值的推推论。 光-Fisher模型的精确度, 其结果在精确性计算方法上与精确度相当, 在可应用的精确度方面, 在可应用的精确度方法中, 在可应用的精确度中, 直观的计算方法中, 必须是。