Examples of using Observed data in English and their translations into Chinese
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The observed data represents incidence of lip cancer in 56 counties in Scotland.
Here is an example observed data set(table 3):.
We have nowcreated a simple Reinforcement Learning model from observed data.
We further assume the each observed data are independent of others.
Part three also elaborates on the use of models and estimates to complement andcomplete the observed data.
Such association can be inferred directly from the observed data using conditional expectation.
In research,there could be a variety of hypotheses that are just as consistent with the observed data.
If the number of observed data points N= 0, then(2.141) reduces to the prior mean as expected.
We see a model as something that describes how observed data is generated.
Prediction is all about using observed data to reason about missing information or future, yet-to-be-observed data. .
The graphical model captures the causal process(Pearl, 1988) by which the observed data was generated.
The observed data are the original unlabeled data and the synthetic data are drawn from a reference distribution.
Here the focus is on“why” questions-in other words finding plausible or probable explanations for observed data.
It examines how well the distribution of observed data fits with the expected distribution in case the variables were independent.
Thus, it is generally impossible to conclusivelychoose between different causal explanations by looking at observed data only.
Very often in Probability and Statistics we will replace observed data or a complex distributions with a simpler, approximating distribution.
As is typical for most crack propagation datasets,the underlying trends are not quite obvious from the observed data points.
Very often in Probability and Statistics we will replace observed data or a complex distributions with a simpler, approximating distribution.
Others are obtained from observation of user activities,or inferred through advanced analysis of volunteered or observed data.
Using the observed data, statistical models are constructed to predict the proportion of bacteria in a sample after going through the sequencing process.
The latent variables are assumed non-gaussian and mutually independent,and they are called independent components of the observed data.
The researchers combined observed data and model predictions to estimate the effect of adding geo-stationary satellite data to aerosol simulations.
The latent variables are assumed non-gaussian and mutually independent,and they are called independent components of the observed data.
In statistics, EM repeats and optimizes the possibilities of finding observed data, while predicting the parameters of a statistical model with unobserved variables.
So, instead of abandoning the standard model,researchers look to“make the models more precisely explain the observed data,” he added.
The theory predicts aboutthree-and-a-half standard deviations difference to the currently observed data, and this disagreement hints towards new, previously misunderstood physics.
More sophisticated statistical inference toolscan be used to quantify the likelihood of observing data samples given an assumption.
After observing data, evidence, or other information, we update our beliefs, and our guess becomes less wrong.