SimTech EXC 2075 Project Network 6 "Machine learning for simulation"
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451 to 460 of 846 Results
simulate_data_and_pmps.Rd
May 25, 2023 - Replication Code for: Meta-Uncertainty in Bayesian Model Comparison
Unknown - 679 B - MD5: fc357468fc11d54cabff4439a3fc4839
simulate_data_bayesian_linear_regression.Rd
May 25, 2023 - Replication Code for: Meta-Uncertainty in Bayesian Model Comparison
Unknown - 512 B - MD5: 18788fbf4268c67c6b8a0693ad791bb3
SIR.Rd
May 25, 2023 - Replication Code for: Meta-Uncertainty in Bayesian Model Comparison
Unknown - 524 B - MD5: e746eb30a13ba4ab677762a898d06bc2
sir_simulator.R
May 25, 2023 - Replication Code for: Meta-Uncertainty in Bayesian Model Comparison
R Syntax - 801 B - MD5: 62d960b88fb577d9128a6d90e9d0092e
test-log_sum_exp.R
May 25, 2023 - Replication Code for: Meta-Uncertainty in Bayesian Model Comparison
R Syntax - 117 B - MD5: 2b0519a5f7be7f5bf1d6fb09d4f6aeb7
testthat.R
May 25, 2023 - Replication Code for: Meta-Uncertainty in Bayesian Model Comparison
R Syntax - 84 B - MD5: c9e4897898f5627111b8ce12e5f4daaf
Data and Code for: Meta-Uncertainty in Bayesian Model Comparison(Universität Stuttgart)
May 17, 2023
Meta-Uncertainty represents a fully probabilistic framework for quantifying the uncertainty over Bayesian posterior model probabilities (PMPs) using meta-models. Meta-models integrate simulated and observed data into a predictive distribution for new PMPs and help reduce overconfidence and estimate the PMPs in future replication studies.
algorithms.py
Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]
Python Source Code - 26.9 KB - MD5: 8992d130a29ce591cf770f831365e711
analysis.py
Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]
Python Source Code - 24.0 KB - MD5: e7e55610a01b7baf79167d03185187bb
benchmark.ipynb
Apr 5, 2023 - Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]
Jupyter Notebook - 116.3 KB - MD5: b64ba74944c8c63b6cdbcb5c3f3c3ca5
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