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Showing 1 to 3 of 3 for “"Metropolis-Hastings within Gibbs"”.

  1. Investigating the performance of process-observation-error-estimator and robust estimators in surplus production model: a simulation study

    … This study used Bayesian method, revised Metropolis Hastings within Gibbs sampling algorithm (MHGS) that was previously used to solve POE_N (Millar and Meyer, 2000), developed the MHGS for the other estimators, and developed the methodologies which enabled all the estimators to deal with …

    vt Repository record for Investigating the performance of process-observation-error-estimator and robust estimators in surplus production model: a simulation study (opens in a new tab)

  2. Enhancement of unconventional oil and gas production forecasting using mechanistic-statistical modeling

    … model is enabled by designing a unique Metropolis-Hastings within Gibbs scheme to take advantage of the model's structure. This novel mechanistic-statistical approach is able to learn and generalize physical relationships across ensembles of wells with vastly different …

    mit Repository record for Enhancement of unconventional oil and gas production forecasting using mechanistic-statistical modeling (opens in a new tab)

  3. The effects of three different priors for variance parameters in the normal-mean hierarchical model

    Many prior distributions are suggested for variance parameters in the hierarchical model. The “Non-informative” interval of the conjugate inverse-gamma prior might cause problems. I consider three priors – conjugate inverse-gamma, log-normal and truncated normal for the variance parameters and do …

    texas Repository record for The effects of three different priors for variance parameters in the normal-mean hierarchical model (opens in a new tab)