Elements of information theory
Elements of information theory
The weighted histogram analysis method for free-energy calculations on biomolecules. I: The method
Journal of Computational Chemistry
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Stability of Stochastic Approximation under Verifiable Conditions
SIAM Journal on Control and Optimization
Journal of Computational Physics
Minimax Entropy Principle and Its Application to Texture Modeling
Neural Computation
Path sampling with stochastic dynamics: Some new algorithms
Journal of Computational Physics
On population-based simulation for static inference
Statistics and Computing
Interacting sequential Monte Carlo samplers for trans-dimensional simulation
Computational Statistics & Data Analysis
Introduction to Nonparametric Estimation
Introduction to Nonparametric Estimation
Accurate Uncertainty Quantification Using Inaccurate Computational Models
SIAM Journal on Scientific Computing
Applications of a Kushner and Clark lemma to general classes of stochastic algorithms
IEEE Transactions on Information Theory
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The present paper proposes an adaptive biasing potential technique for the computation of free energy landscapes. It is motivated by statistical learning arguments and unifies the tasks of biasing the molecular dynamics to escape free energy wells and estimating the free energy function, under the same objective of minimizing the Kullback-Leibler divergence between appropriately selected densities. It offers rigorous convergence diagnostics even though history dependent, non-Markovian dynamics are employed. It makes use of a greedy optimization scheme in order to obtain sparse representations of the free energy function which can be particularly useful in multidimensional cases. It employs embarrassingly parallelizable sampling schemes that are based on adaptive Sequential Monte Carlo and can be readily coupled with legacy molecular dynamics simulators. The sequential nature of the learning and sampling scheme enables the efficient calculation of free energy functions parametrized by the temperature. The characteristics and capabilities of the proposed method are demonstrated in three numerical examples.