Network inference in stochastic systems from neurons to currencies: Improved performance at small sample size.

ABSTRACT: The fundamental problem in modeling complex phenomena such as human perception using probabilistic methods is that of deducing a stochastic model of interactions between the constituents of a system from observed configurations. Even in this era of big data, the complexity of the systems being modeled implies that inference methods must be effective in the difficult regimes of small sample sizes and large coupling variability. Thus, model inference by means of minimization of a cost function requires additional assumptions such as sparsity of interactions to avoid overfitting. In this paper, we completely divorce iterative model updates from the value of a cost function quantifying goodness of fit. This separation enables the use of goodness of fit as a natural rationale for terminating mo

SUBMITTER: Hoang DT 

PROVIDER: S-EPMC7459391 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature