Adding Constraints to Bayesian Inverse Problems
Authors: Jiacheng Wu, Jian-Xun Wang, Shawn C. Shadden1666-1673
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A synthetic model is presented to demonstrate the effectiveness of the proposed method and in both the exact Bayesian inference and ensemble Kalman filter scenarios, numerical simulations show that imposing constraints using the method presented improves identification of the true parameter solution among multiple local minima. |
| Researcher Affiliation | Academia | Jiacheng Wu University of California Berkeley, CA, 94706 Jian-Xun Wang University of Notre Dame Notre Dame, IN, 46556 Shawn C. Shadden University of California Berkeley, CA, 94706 |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about open-sourcing the code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | A synthetic model is presented to demonstrate the effectiveness of the constrained Bayesian inference framework. ... We assume the observed data follow the normal distribution N( y, Σl) where the mean y = 1.0 and the covariance matrix Σl is chosen based on the uncertainty associated with data. |
| Dataset Splits | No | The paper mentions using J=5000 samples for the prior distribution but does not specify any train/validation/test dataset splits or cross-validation setup for experimental reproduction. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used to run the experiments (e.g., CPU, GPU models, memory, or cluster specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for replication (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | The prior distribution (6) is first sampled with J = 5000 samples. The mean and the covariance matrix are set to ... The variance for the constraint is set to be Σc = 0.5... The covariance matrix of the prior and the covariance of the data likelihood are given as Σθ = [1, 0; 0, 1] and Σl = 0.01. The covariance of the constraint used here is Σc = 2.0... |