mlsanityData to 14 Oct 2026

An Optimal Likelihood Free Method for Biological Model Selection

arXiv 2208.02344 ↗First posted 3 Aug 2022q-bio.QM stat.MLcited by 0

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Abstract (arXiv)

Systems biology seeks to create math models of biological systems to reduce inherent biological complexity and provide predictions for applications such as therapeutic development. However, it remains a challenge to determine which math model is correct and how to arrive optimally at the answer. We present an algorithm for automated biological model selection using mathematical models of systems biology and likelihood free inference methods. Our algorithm shows improved performance in arriving at correct models without a priori information over conventional heuristics used in experimental biology and random search. This method shows promise to accelerate biological basic science and drug discovery.

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References · 16

  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  2. 2021Convex optimizationcited by 2,515
  3. 2015Variational Inference with Normalizing Flowscited by 2,170
  4. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,079
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  6. 2018MINE: Mutual Information Neural Estimationcited by 683
  7. 2018Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flowscited by 221
  8. 2019Automatic Posterior Transformation for Likelihood-Free Inferencecited by 219
  9. 2019A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experimentscited by 58
  10. 2020Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimationcited by 56
  11. 2016Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimationcited by 52
  12. 2022Variational methods for simulation-based inferencecited by 43
4 more
  1. 2021Sequential Neural Posterior and Likelihood Approximationcited by 30
  2. 2021Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Boundscited by 27
  3. 2020Amortized Bayesian model comparison with evidential deep learningcited by 22
  4. 2021Sparse MoEs meet Efficient Ensemblescited by 19

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