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Decoding stimulus-specific regulation of promoter activity of p53 target gene...
All measurements derived from smFISH experiments as well as additional output from Bayesian inference are publicly available via the institutional repository of Technical... -
Decoding stimulus-specific regulation of promoter activity of p53 target gene...
All measurements derived from smFISH experiments as well as additional output from Bayesian inference are publicly via the institutional repository of Technical University... -
Sampling Strategies of the Regime-and-memory model (RMM)
This excel file includes the observation time, Q, concentration, and lag-time used by the sampling strategies. Types of sampling strategies: Time frequency sampling... -
Regime-and-memory model (RMM) Code
We introduce a simple stochastic time-series model (regime-and-memory model, RMM) for concentrations in the river that accounts for fluctuating release and transport with... -
Auxiliary data set for Mäkinen et al. (2021) Bayesian Classification of Meteo...
Auxiliary data for the paper Mäkinen et al. 2021. Bayesian Classification of Meteorological and Non-Meteorological Targets in Polarimetric Weather Radar Measurements. Submitted... -
Sampling Strategies of the Regime-and-memory model (RMM)
This excel file includes the observation time, Q, concentration, and lag-time used by the sampling strategies. Types of sampling strategies: Time frequency sampling... -
Regime-and-memory model (RMM) Code
We introduce a simple stochastic time-series model (regime-and-memory model, RMM) for concentrations in the river that accounts for fluctuating release and transport with... -
RoCELL: Robust Causal Estimation in the Large-Sample Limit without Strict Fai...
In the era of big data, the increasing availability of huge data sets can paradoxically be harmful when our causal inference method is designed to search for a causal model that... -
MASSIVE: Model Assessment and Stochastic Search for Instrumental Variable Est...
The recent availability of huge, many-dimensional data sets, like those arising from genome-wide association studies (GWAS), provides many opportunities for strengthening causal... -
BFCS: Bayes Factors of Covariance Structures
Gene regulatory networks play a crucial role in controlling an organism’s biological processes, which is why there is significant interest in developing computational methods... -
Fast Bayesian force fields from active learning: study of inter-dimensional t...
Gaussian process (GP) regression is one promising technique of constructing machine learning force fields with built-in uncertainty quantification, which can be used to monitor... -
On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomist...
Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training... -
Fast Bayesian force fields from active learning: study of inter-dimensional t...
Gaussian process (GP) regression is one promising technique of constructing machine learning force fields with built-in uncertainty quantification, which can be used to monitor... -
Fast Bayesian force fields from active learning and mapped Gaussian processes...
Gaussian process (GP) regression is one promising technique of constructing machine learning force fields with built-in uncertainty quantification, which can be used to monitor...
