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      <title>Second Brain</title>
      <link>https://redam94.github.io/second-brain</link>
      <description>Last 10 notes on Second Brain</description>
      <generator>Quartz -- quartz.jzhao.xyz</generator>
      <item>
    <title>Approximate Bayesian Computation for ABMs</title>
    <link>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Calibration-Methods/Approximate-Bayesian-Computation-for-ABMs</link>
    <guid>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Calibration-Methods/Approximate-Bayesian-Computation-for-ABMs</guid>
    <description><![CDATA[ Approximate Bayesian Computation for ABMs Summary Approximate Bayesian Computation (ABC) calibrates ABMs by sampling parameter sets from a prior, running the model, and accepting samples where the model error is below a threshold \varepsilon. ]]></description>
    <pubDate>Sun, 28 Jun 2026 05:35:31 GMT</pubDate>
  </item><item>
    <title>History Matching for ABMs</title>
    <link>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Calibration-Methods/History-Matching-for-ABMs</link>
    <guid>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Calibration-Methods/History-Matching-for-ABMs</guid>
    <description><![CDATA[ History Matching for ABMs Summary History Matching (HM) is an iterative wave-based procedure that eliminates implausible parameter regions for an ABM. ]]></description>
    <pubDate>Sun, 28 Jun 2026 05:35:31 GMT</pubDate>
  </item><item>
    <title>Uncertainty Quantification for ABM Calibration</title>
    <link>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Calibration-Methods/Uncertainty-Quantification-for-ABM-Calibration</link>
    <guid>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Calibration-Methods/Uncertainty-Quantification-for-ABM-Calibration</guid>
    <description><![CDATA[ Uncertainty Quantification for ABM Calibration Summary Calibrating ABMs requires explicitly identifying and quantifying four sources of uncertainty: parameter uncertainty (unknown parameters), model discrepancy (model is an imperfect abstraction), ensemble variance (stochastic variation across runs)... ]]></description>
    <pubDate>Sun, 28 Jun 2026 05:35:31 GMT</pubDate>
  </item><item>
    <title>Population Initialization and Parameter Sensitivity</title>
    <link>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Experimental-Design/Population-Initialization-and-Parameter-Sensitivity</link>
    <guid>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Experimental-Design/Population-Initialization-and-Parameter-Sensitivity</guid>
    <description><![CDATA[ Population Initialization and Parameter Sensitivity Summary Karakaya et al. ]]></description>
    <pubDate>Sun, 28 Jun 2026 05:35:31 GMT</pubDate>
  </item><item>
    <title>Global Sensitivity Analysis - Overview</title>
    <link>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Sensitivity-Analysis/Global-Sensitivity-Analysis---Overview</link>
    <guid>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Sensitivity-Analysis/Global-Sensitivity-Analysis---Overview</guid>
    <description><![CDATA[ Global Sensitivity Analysis - Overview Summary Global sensitivity analysis (GSA) apportions the uncertainty in a model’s output across its uncertain inputs, with all inputs varied simultaneously across their full ranges. ]]></description>
    <pubDate>Sun, 28 Jun 2026 05:35:31 GMT</pubDate>
  </item><item>
    <title>Local vs Global Sensitivity Analysis</title>
    <link>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Sensitivity-Analysis/Local-vs-Global-Sensitivity-Analysis</link>
    <guid>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Sensitivity-Analysis/Local-vs-Global-Sensitivity-Analysis</guid>
    <description><![CDATA[ Local vs Global Sensitivity Analysis Summary Local sensitivity analysis (LSA / OAT) perturbs one parameter at a time around a fixed baseline, measuring the local derivative \partial f/\partial x_i. ]]></description>
    <pubDate>Sun, 28 Jun 2026 05:35:31 GMT</pubDate>
  </item><item>
    <title>Morris Elementary Effects Screening</title>
    <link>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Sensitivity-Analysis/Morris-Elementary-Effects-Screening</link>
    <guid>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Sensitivity-Analysis/Morris-Elementary-Effects-Screening</guid>
    <description><![CDATA[ Morris Elementary Effects Screening Summary The Morris method is a derivative-based screening technique. ]]></description>
    <pubDate>Sun, 28 Jun 2026 05:35:31 GMT</pubDate>
  </item><item>
    <title>Sampling and Estimation for Sobol Indices</title>
    <link>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Sensitivity-Analysis/Sampling-and-Estimation-for-Sobol-Indices</link>
    <guid>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Sensitivity-Analysis/Sampling-and-Estimation-for-Sobol-Indices</guid>
    <description><![CDATA[ Sampling and Estimation for Sobol Indices Summary Sobol indices are integrals that must be estimated from model runs. ]]></description>
    <pubDate>Sun, 28 Jun 2026 05:35:31 GMT</pubDate>
  </item><item>
    <title>Variance-Based Sensitivity and Sobol Indices</title>
    <link>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Sensitivity-Analysis/Variance-Based-Sensitivity-and-Sobol-Indices</link>
    <guid>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Sensitivity-Analysis/Variance-Based-Sensitivity-and-Sobol-Indices</guid>
    <description><![CDATA[ Variance-Based Sensitivity and Sobol Indices Summary Sobol’s method decomposes the output variance V(Y) into contributions from individual factors and their interactions (the ANOVA / HDMR / Sobol-Hoeffding decomposition), assuming inputs are independent and uncorrelated. ]]></description>
    <pubDate>Sun, 28 Jun 2026 05:35:31 GMT</pubDate>
  </item><item>
    <title>Index: Sensitivity Analysis</title>
    <link>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Sensitivity-Analysis/_Index</link>
    <guid>https://redam94.github.io/second-brain/Research/Agent-Based-Modeling/Calibration-and-Validation/Sensitivity-Analysis/_Index</guid>
    <description><![CDATA[ Sensitivity Analysis Routing Summary This folder covers global sensitivity analysis (GSA) for ABM parameter spaces — variance-based Sobol indices, Morris screening, and Saltelli/FAST estimation. ]]></description>
    <pubDate>Sun, 28 Jun 2026 05:35:31 GMT</pubDate>
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