The phrase "probabilistic prediction" is grammatically correct and can be used in written English.
It refers to a prediction or forecast that is based on probabilities or likelihoods rather than certainties. This term is commonly used in fields such as statistics, weather forecasting, and finance. Example: The meteorologist made a probabilistic prediction that there would be a 30% chance of rain tomorrow based on the current conditions.
We will also use the methods of probabilistic prediction on the phishing websites to predict potential target website based solely on the URL of the phishing website.
A scoring rule provides a summary measure for evaluating probabilistic prediction by assigning a numerical score based on the predictive distribution and on the event or value that materializes (Garthwaite et al. 2005; Kneib et al. 2007).
Probabilistic prediction algorithms that exploit domain knowledge of Mendelian inheritance and other biological characteristics of susceptibility genes have successfully contributed to improved screening, prevention, and genetic testing.
Data scientists come up with probabilistic prediction models for specific actions, and then the algorithm churns through loads of data to produce better decisions in real time with every iteration.
The probabilistic prediction of shear strength properties was established by the combinations of the best predictors using Bayes's theorem.
An approach for probabilistic prediction of fatigue crack initiation lifetime of structural details and mechanical components is presented.
Differently from other proposed methods [ 2- 10], which are based on predictive models and provide probabilistic predictions, the simple, yet effective, transitive relationship approach is based on logic rules [ 11].
The question is therefore not 'how well can we predict disease' but 'how can we incorporate probabilistic predictions of disease in personal or clinical decision making'.
Robust predictive analyses use the entire model class with the probabilistic predictions of each model being weighted by its prior probability, or if structural response data is available, by its posterior probability from Bayes' Theorem for the model class.
This paper presents a new replanning paradigm - Probabilistic, Prediction-based Replanning - suitable for complex, uncertain, dynamic domains.
More recent research has shown probabilistic predictions to be more accurate than temporal predictions.
