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Found 10 papers, 6 papers with code
On the intrinsic dimensionality of Covid-19 data: a global perspective
This paper aims to develop a global perspective of the complexity of the relationship between the standardised per-capita growth rate of Covid-19 cases, deaths, and the OxCGRT Covid-19 Stringency Index, a measure describing a country's stringency of lockdown policies.
Learning Summary Statistics for Bayesian Inference with Autoencoders
For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of observations with simulated model outputs in terms of a small set of summary statistics.
Revealing three-dimensional quantum criticality by Sr-substitution in Han Purple
Classical and quantum phase transitions (QPTs), with their accompanying concepts of criticality and universality, are a cornerstone of statistical thermodynamics.
Strongly Correlated Electrons
Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning
Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies.
A Common Atom Model for the Bayesian Nonparametric Analysis of Nested Data
The use of high-dimensional data for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population.
Methodology Applications
Data segmentation based on the local intrinsic dimension
Here we develop a robust approach to discriminate regions with different local IDs and segment the points accordingly.
Regularized Zero-Variance Control Variates
Zero-variance control variates (ZV-CV) are a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target.
Computation Methodology
Fast Maximum Likelihood estimation via Equilibrium Expectation for Large Network Data
We demonstrate the performance of the EE algorithm in the context of exponential random graphmodels (ERGMs) a family of statistical models commonly used in empirical research based on network data observed at a single period in time.
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment and to extend the library with new algorithms.
Computation
Bayesian Inference of Spreading Processes on Networks
Although the underlying processes of transmission are different, the network approach can be used to study the spread of pathogens in a contact network or the spread of rumors in an online social network.
