Learning Dynamic Models from Non-sequenced Data
نویسندگان
چکیده
Learning Dynamic Models ❑ Useful for analyzing time-evolving data ❛ Hidden Markov Models ❛ Dynamic Bayesian Networks ❛ System Identification ❛ Key assumption: SEQUENCED observations What if observations are NOT SEQUENCED? ❑ Galaxy evolution (many snapshots, no ordering) ❑ Slow-developing diseases (e.g., Alzheimer’s) ❑ Destructive measurement for biological processes Example: Gene Expression Time Series (Tu et al., Science 2005)
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تاریخ انتشار 2010