A reversible infinite HMM using normalised random measures
Authors: David Knowles, Zoubin Ghahramani, Konstantina Palla
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use our process to construct a reversible infinite HMM which we apply to two real datasets, one from epigenomics and one ion channel recording.In this section we evaluate the SHGP by running the SHGP Hidden Markov model on two real world datasets. |
| Researcher Affiliation | Academia | Konstantina Palla KP376@CAM.AC.UK University of Cambridge, Trumpington Street, CB2 1PZ David A. Knowles DAVIDKNOWLES@CS.STANFORD.EDU Stanford University, 353 Serra Mall, CA 94305-9025 Zoubin Ghahramani ZOUBIN@ENG.CAM.AC.UK University of Cambridge, Trumpington Street, CB2 1PZ |
| Pseudocode | No | The paper describes the inference steps in paragraph form and bullet points but does not contain formally structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | For this experiment we used Ch IP-seq (chromatin immunoprecipitation sequencing) data... (see Park (2009) for a nice review).and analysing a 1MHz recording from the state-of-the-art method of Rosenstein et al. (2013) of a single alamethicin channel. |
| Dataset Splits | Yes | We ran 10 repeats, each time holding out different 20% of the data Y and using different random initilisation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions several algorithms and methods used (e.g., slice sampling, HMC, NUTS, beam sampler, forward-backward algorithm) but does not provide specific software names with version numbers or dependencies for replication. |
| Experiment Setup | Yes | We used a truncation level of K = 20, 1000 iterations and a burnin of 700. and For SHGP-HMM we used a truncation of K = 15, 1000 iterations and a burnin of 700. 50 inner iterations of HMC or NUTS were run per outer iteration. |