Research map / Probabilistic and scientific modeling / Dynamical systems
Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use time-warped ARHMM instead of 10-state ARHMM with K=2 states | T-WARHMM · Simulated rotation data | test log-likelihood: 0.037 -> 0.066 | Helped |
| replace time-warp linear modulation with GP nonlinear modulation (ablation) | WARHMM · Simulated rotation data | test log-likelihood / recovery of states: 0.066 -> 0.024 | Mixed |
| add warping variable to ARHMM | ARHMM · MoSeq mouse depth video | number of syllables for similar test LL: 40 -> 25 syllables (T-WARHMM), 10 (GP-WARHMM) | Helped |
| use GP warping instead of time warping (ablation) | WARHMM · MoSeq mouse depth video | test log-likelihood | Helped |
| use T-WARHMM instead of ARHMM for rear syllables | T-WARHMM · MoSeq mouse depth video | number of rear syllables | Helped |
| amphetamine treatment vs saline on inferred vigor | T-WARHMM · Saline vs amphetamine mice | mean tau index | Helped |
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