Probabilistic size-and-shape functional mixed models
Authors: Fangyi Wang, Karthik Bharath, Oksana Chkrebtii, Sebastian Kurtek
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments demonstrate utility of the proposed model, and superiority over the current state-of-the-art. We present posterior inference results from the model described in Section 3 for simulated and real data. |
| Researcher Affiliation | Academia | Fangyi Wang Department of Statistics The Ohio State University Columbus, OH, 43210 wang.15022@osu.edu Karthik Bharath School of Mathematical Sciences University of Nottingham Nottingham, UK, NG7 2RD Karthik.Bharath@nottingham.ac.uk Oksana Chkrebtii Department of Statistics The Ohio State University Columbus, OH, 43210 oksana@stat.osu.edu Sebastian Kurtek Department of Statistics The Ohio State University Columbus, OH, 43210 kurtek.1@stat.osu.edu |
| Pseudocode | Yes | The detailed MCMC algorithm is given in Algorithm 1. |
| Open Source Code | Yes | The supplementary material include data and code for reproducibility purposes. |
| Open Datasets | Yes | Consider a sample of functions from the much-studied Berkeley growth data in Figure 1(a)... We now consider application of the proposed modeling framework to (i) Berkeley growth rate functions (n = 93) [Srivastava et al., 2011b], and (ii) PQRST complexes (n = 40) [Kurtek et al., 2013]. |
| Dataset Splits | No | The paper conducts numerical experiments on simulated and real data but does not specify any training, validation, or test splits. It mentions MCMC burn-in periods but this relates to sampling, not data partitioning. |
| Hardware Specification | Yes | To evolve the MCMC algorithm in MATLAB R2021a for 300, 000 iterations yielding 100, 000 posterior samples after burn-in, on a computing server with 6 parallel Intel(R) Xeon(R) CPUs with 20GB of memory, the computing time is approximately 93 and 111 minutes, respectively, under PMs 1 and 2 on Γ, based on n = 30 functions discretized at T = 50 points. |
| Software Dependencies | Yes | To evolve the MCMC algorithm in MATLAB R2021a for 300, 000 iterations yielding 100, 000 posterior samples after burn-in... |
| Experiment Setup | Yes | We use Bf = 6 modified Fourier basis functions for µ and Br = 6 B-spline basis functions for each vi. The ground truth variances are σ2 c = 0.25 and σ2 = 0.1. ... For a, σ2 c and σ2, we use weakly informative prior distributions: a MVN(0, 10000IBf ), σ2 c IG(0.01, 0.01), σ2 IG(0.01, 0.01)... We use θγ = 30 in all of our numerical experiments. ... For all examples in this section, we use N = 100, 000 with a burn-in period of 200, 000 iterations. |