The Fixed Landscape Inference MethOd (flimo): a versatile alternative to Approximate Bayesian Computation, faster by several orders of magnitude
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固定景观推断方法 flimo：近似贝叶斯计算的高效替代方案
- 研究问题
- 对于可模拟但似然难以计算的连续状态随机模型，能否降低参数推断的计算成本，同时保持与现有方法相当的准确性？
- 方法与思路
- 作者提出无似然方法 flimo，以确定性梯度优化最小化观测与模拟摘要统计量的差异，并可近似参数分布。
- 材料陈述的贡献
- 摘要称，前两个应用中推断耗时大幅下降且准确性相当；另展示混沌种群模型应用，并提供 Julia 与 R 实现。
- 阅读关注点
- 应核对固定景观的具体构造、摘要统计量选择、梯度计算和参数分布近似方式，以及各应用的准确性与耗时比较。
- 阅读边界
- 所据为 arXiv 预印本摘要，同行评审与期刊接收未核实；摘要未提供收敛条件、分布近似保证或加速的具体数值。
原文摘要
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.