Estimating Latent-Variable Graphical Models using Moments and Likelihoods
Authors: Arun Tejasvi Chaganty, Percy Liang
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 4. Comparison of parameter estimation error (ˆθ − θ 2) versus error in moments (ϵ) for a hidden Markov model with k = 2 hidden and d = 5 observed values. Empirical moments c M123 were generated by adding Gaussian noise, N(0, ϵI), to expected moments M123. Results are averaged over 400 trials. |
| Researcher Affiliation | Academia | Arun Tejasvi Chaganty CHAGANTY@CS.STANFORD.EDU Percy Liang PLIANG@CS.STANFORD.EDU Stanford University, Stanford, CA, USA |
| Pseudocode | Yes | Algorithm 1 GETMARGINALS (pseudoinverse) |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the methodology described, nor does it provide any links to a code repository. |
| Open Datasets | No | Empirical moments c M123 were generated by adding Gaussian noise, N(0, ϵI), to expected moments M123. |
| Dataset Splits | No | The paper discusses the comparison of statistical efficiency between estimators using generated empirical moments over 'n data points' (Section 4.3), but it does not specify train/validation/test dataset splits for reproduction. |
| Hardware Specification | No | The paper mentions numerical experiments were performed and results averaged over 400 trials, but it does not provide any specific hardware details such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper refers to optimization procedures like 'EM or LBFGS' but does not specify any software libraries, packages, or their version numbers that were used for implementation or experimentation. |
| Experiment Setup | Yes | Comparison of parameter estimation error (ˆθ − θ 2) versus error in moments (ϵ) for a hidden Markov model with k = 2 hidden and d = 5 observed values. Empirical moments c M123 were generated by adding Gaussian noise, N(0, ϵI), to expected moments M123. |