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A past observation shows that the sodium atmosphere of the Moon changes in synchronization with Leonids (Smith et al, 1999; Shiokawa et al., 2000).
The set of states in variable-order Markov model in this case is a subset S of Λ={0,1,00,10,01,11}, chosen by an algorithm run on a past observation sequence.
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Therefore, those training pairs are prepared from past observation times, e.g., a few days before the target day for which prediction error degrees are estimated.
The function with a fractal component is fitted to the past observation data.
These explanations are good to the extent that they jibe with past observation and are able to predict future events.
Therefore, each CUpredict houses the primary channel's past observation.
Statistical models, by contrast, do not use physical equations, but rather statistical formulations that produce forecasts based on a long history of past observations of (a) what is being predicted (e.g., the Niño3.4 SST anomaly), and (b) relevant predictors (e.g., sea level pressure patterns, tropical Pacific subsurface temperatures, or even the Niño3.4 SST itself).
Their workload predictor is based on a technique that uses past observations of a workload to predict the peak demand that will be seen over a period of T time units.
It also offers a reinterpretation of past observations.
It formulates the conditional distribution of each measurement as a function of past observations and relevant risk factors.
Grounding a command necessitates a representa- tion for past observations and interactions; however, maintaining the full context consisting of all pos- sible observed objects, attributes, spatial relations, actions, etc., over time is intractable.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com