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Typically, this is done with historical data and involves experimenting with different models.
The context is observed via sensor and self-report data and involves, for example, current and past user location, weather, social setting, user stress and mood, user behaviors and user engagement.
In most cases, however, these applications are limited to collecting and displaying data and involves limited use of targeted spatial analysis functions (Hall 2015).
It makes the best use of the available data and involves no random subsampling.
The former is better able to explain the observed data and involves animals close to clinical onset being more likely than their age-matched peers to die on farm or be slaughtered.
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GMMs are generative models for real-world data and involve two advantages in our application.
W-GS participated in the design of study, acquisition, analysis, and interpretation of data, and involved in drafting the manuscript.
Thematic analysis provided a structure for identifying patterns in data and involved steps of data codification; testing and connecting codes, searching and establishing themes; reviewing and corroborating themes.
SK collected and analysed data and involved in drafting manuscript.
HXZ, GZN, HYF and WDR collected the patient data and involved in the interpretation of data.
GA has contributions to conception and design, acquisition of data and involved in drafting the manuscript.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com