Research map / Probabilistic and scientific modeling / Treatment effects
Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase sample size from 50 to 150 | Piecewise-logistic MCMC-SAEM · Synthetic data | relative error of sigma: 25.24 -> 2.83 | Helped |
| increase sample size from 50 to 150 | Piecewise-logistic MCMC-SAEM · Synthetic data | relative error of gamma_init: 1.63 -> 2.14 | No clear effect |
| increase sample size from 50 to 150 | Piecewise-logistic MCMC-SAEM · Synthetic data | relative error of t_1: 4.41 -> 3.42 | Mixed |
| increase sample size from 50 to 150 | Piecewise-logistic MCMC-SAEM · Synthetic data | relative error of t_R: 11.58 -> 9.24 | Mixed |
| increase sample size from 50 to 150 | Piecewise-logistic MCMC-SAEM · Synthetic data | relative error of gamma_escap: 9.45 -> 11.40 | No clear effect |
| increase sample size from 50 to 150 | Piecewise-logistic MCMC-SAEM · Synthetic data | relative error of gamma_fin: 6.23 -> 5.82 | No clear effect |
| increase sample size n | Piecewise-logistic MCMC-SAEM · SAEM mean trajectory | gap between gamma_0 and trajectory of average parameters | Helped |
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