Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace stationary/non-stationary Cox models with semi-stationary MMGCP (Markov-modulated GP intensities) | Cox process intensity model · Synthetic Semi-Stn | average test log-likelihood: MMGCP −87.66 vs GPCox −97.64, MMPP −90.66 | Helped |
| Use MMGCP instead of GPCox/MMPP baselines | Cox process intensity model · Football shot attempts | average test log-likelihood: −69.48 vs MMPP −70.34, GPCox −72.26 | Helped |
| Use MMGCP instead of GPCox/MMPP baselines | Cox process intensity model · Italy's earthquakes | average test log-likelihood: −101.19 vs MMPP −117.17, GPCox −109.73 | Helped |
| Use MMPP (stationary) instead of MMGCP | Cox process intensity model · Synthetic Full-Stn | average test log-likelihood: MMPP 15.45 vs MMGCP 2.50 | Hurt |
| Use MMGCP instead of GPCox on fully non-stationary data | Cox process intensity model · Synthetic Non-Stn | average test log-likelihood: −42.19 vs −42.36 for GPCox | No clear effect |
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