discreteness assumption
ensure discreteness
discreteness level
modeling discreteness
discreteness property
high discreteness
assessing discreteness
discreteness constraints
introducing discreteness
discreteness analysis
the discreteness of the data points was a key factor in choosing the appropriate statistical test.
we analyzed the discreteness of the signal to identify potential noise sources.
the model's ability to capture the discreteness of consumer behavior was crucial for accurate forecasting.
discreteness in the distribution of income highlights the need for progressive taxation.
the discreteness of the sampling method introduced a degree of uncertainty into the results.
understanding the discreteness of the population is vital for targeted marketing campaigns.
the discreteness of the events made it difficult to establish a clear causal relationship.
we assessed the discreteness of the algorithm's output to evaluate its performance.
the discreteness of the variables required a non-parametric approach to analysis.
despite the discreteness, patterns emerged when we analyzed the data over time.
the discreteness of the options available influenced the customer's final decision.
discreteness assumption
ensure discreteness
discreteness level
modeling discreteness
discreteness property
high discreteness
assessing discreteness
discreteness constraints
introducing discreteness
discreteness analysis
the discreteness of the data points was a key factor in choosing the appropriate statistical test.
we analyzed the discreteness of the signal to identify potential noise sources.
the model's ability to capture the discreteness of consumer behavior was crucial for accurate forecasting.
discreteness in the distribution of income highlights the need for progressive taxation.
the discreteness of the sampling method introduced a degree of uncertainty into the results.
understanding the discreteness of the population is vital for targeted marketing campaigns.
the discreteness of the events made it difficult to establish a clear causal relationship.
we assessed the discreteness of the algorithm's output to evaluate its performance.
the discreteness of the variables required a non-parametric approach to analysis.
despite the discreteness, patterns emerged when we analyzed the data over time.
the discreteness of the options available influenced the customer's final decision.
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