Active Inference for Dynamic Bayesian Networks
Authors: Caner Komurlu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, our proposed method, active inference for dynamic Bayesian networks, will be described and evaluated on two practical problems: i) detecting optimal time for observation for tissue engineering, and ii) dynamic detection of optimal observation subset on wireless sensor networks. [...] Our results show that on smaller B, e.g. %10 of all sensors, GP and KF yield smaller error than our DBN as they utilize local attributes. On larger B, our DBN outperforms the baseline models. |
| Researcher Affiliation | Academia | Caner Komurlu Illinois Institute of Technology, Chicago Illinois ckomurlu@hawk.iit.edu |
| 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 or link regarding the public availability of source code for the methodology described. |
| Open Datasets | Yes | We used Intel Lab Data [Deshpande et al., 2004]. |
| Dataset Splits | No | The paper mentions using "training data" but does not provide specific details on how the data was split into training, validation, and test sets (e.g., percentages, sample counts, or k-fold cross-validation details). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the models and methods used but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |