Conditional Independence in Testing Bayesian Networks
Authors: Yujia Shen, Haiying Huang, Arthur Choi, Adnan Darwiche
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we illustrate our results on a number of concrete examples, including a case study on Hidden Markov Models. We simulated examples from a third-order HMM and trained both an HMM and a Testing HMM using the structure in Figure 8(a). The cross entropy loss was used to train both the HMM and the Testing HMM using an AC and a TAC, respectively. Our goal was to demonstrate the extent to which a Testing HMM can compensate the modeling error, i.e., the missing dependencies of Ht on Ht 2 and Ht 3. We used data sets with 16, 384 records for each run and 5-fold cross validation to report prediction accuracy as shown in Figure 9. |
| Researcher Affiliation | Academia | 1Computer Science Department, University of California, Los Angeles, California, USA. Correspondence to: Yujia Shen <yujias@cs.ucla.edu>. |
| Pseudocode | No | The paper does not contain explicit pseudocode or algorithm blocks. It describes processes and structures using text and diagrams. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | This is a real-world example comparing the success rates of two treatments for kidney stones (https://en. wikipedia.org/wiki/Simpson%27s_paradox). |
| Dataset Splits | Yes | We used data sets with 16, 384 records for each run and 5-fold cross validation to report prediction accuracy as shown in Figure 9. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for dependencies. |
| Experiment Setup | Yes | We considered all transition models for third-order HMMs such that P(ht | ht 3, ht 2, ht 1) is either 0.95 or 0.05. We assumed binary variables and a chain of length 8. We used uniform initial distributions and emission model P(ht | et) = P( ht | e T ) = 0.99. The cross entropy loss was used to train both the HMM and the Testing HMM using an AC and a TAC, respectively. |