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Found 6 papers, 3 papers with code
Tight basis cycle representatives for persistent homology of large data sets
Here, we provide a strategy and algorithms to compute tight representative boundaries around nontrivial robust features in large data sets.
Dory: Overcoming Barriers to Computing Persistent Homology
We present Dory, an efficient and scalable algorithm that can compute the persistent homology of large data sets.
Inference of stochastic time series with missing data
At the initial iteration of the EM algorithm, the model inference shows better model-data consistency with observed data points than with missing data points.
Inverse Ising inference from high-temperature re-weighting of observations
Maximum Likelihood Estimation (MLE) is the bread and butter of system inference for stochastic systems.
Data-driven inference of hidden nodes in networks
Finally, an important hidden variable problem is to find the number of clusters in a dataset.
Data Analysis, Statistics and Probability Physics and Society
Causality inference in stochastic systems from neurons to currencies: Profiting from small sample size
We introduce a data-driven statistical physics approach to model inference based on minimizing a free energy of data and show superior model recovery for small sample sizes.
Data Analysis, Statistics and Probability Quantitative Methods
