Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Scalable approximate Bayesian inference for particle tracking data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Amortized neural network sampler (filter-backward-sample-forward approximation) instead of deterministic detect+link tracking | particle tracking · Simulated 2D datasets from Chenouard et al. 2014 (SNR=5 and SNR=4) | RMSE, COV, Assign error, JSC (Table 1) | Helped |
| Neural network sampler vs. Utrack and Wilson baselines | particle tracking · Simulated SNR=4 dataset | JSC: 0.82 vs 0.68 (Utrack), 0.63 (Wilson) | Helped |
| Using a Markov approximation of the posterior with local temporal context M=2 frames instead of full-sequence backward information | network sampler · general | qualitative | No clear effect |
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