Infinite Hidden Semi-Markov Modulated Interaction Point Process
Authors: matt zhang, Peng Lin, Peng Lin, Ting Guo, Yang Wang, Yang Wang, Fang Chen
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The approach is tested on both synthetic and real-world data with promising outcomes. |
| Researcher Affiliation | Collaboration | Data61 CSIRO, Australian Technology Park, 13 Garden Street, Eveleigh NSW 2015, Australia School of Computer Science and Engineering, The University of New South Wales, Australia |
| Pseudocode | Yes | The PG sampler is given in the following: Step 1: Initialization, i = 0, set Ω(0), s1:N(0), B1:N(0). Step 2: For iteration i ≥ 1 (a) Sample Ω(i) p{Ω|y1:N, t1:N, s1:N(i − 1)}. (b) Run a conditional SMC algorithm targeting pΩ(i)(s1:N|y1:N, t1:N) conditional on s1:N(i − 1) and B1:N(i − 1). (c) Sample s1:N(i) pΩ(i)(s1:N|y1:N). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. There are no specific repository links, explicit code release statements, or indications of code in supplementary materials. |
| Open Datasets | Yes | In this section, we use energy consumption data from the Reference Energy Disaggregation Dataset (REDD ) [9] to demonstrate the application of the proposed model. |
| Dataset Splits | No | The sequence is divided into two parts 90% and 10%. The first part of the sequence is used for training models. |
| Hardware Specification | No | The paper does not provide specific hardware details (like exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. Only general computing environments are implied by the nature of the research. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | The background intensity is set to 0.6 and the triggering kernels take the exponential form: λ(t) = µ + P tn<t α exp( β (t tn)) with {0.1, 0.9}, {0.5, 0.9}, {0.1, 0.6}, {0.5, 0.6} as the {α , β } parameter pairs of the kernels. |