Computational Models
Computational models are mathematical algorithms and computer programs that simulate real-world systems and processes. These models are used in data science and artificial intelligence to analyze and predict complex phenomena, such as weather patterns, financial markets, and biological systems. Computational models can be based on statistical methods, machine learning algorithms, or other mathematical techniques. They are often used to test hypotheses, generate predictions, and optimize decision-making. Computational models can be used to simulate the behavior of complex systems, such as the human brain, and to develop new technologies, such as self-driving cars and virtual assistants.
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- Hypothesis Testing: Hypothesis testing is a statistical method used to determine whether a hypothesis about a population parameter is supported by the sample data. It involves formulating a null hypothesis and an alternative hypothesis, selecting a significanc ... Read More >>
- Aliasing: Aliasing is a phenomenon that occurs when a signal is sampled at a rate that is too low to accurately represent the original signal. This results in a distorted representation of the signal, where high-frequency components are misrepresente ... Read More >>
- K-fold Cross-validation: K-fold cross-validation is a technique used in machine learning to evaluate the performance of a model. It involves dividing the dataset into k equal parts or folds. The model is trained on k-1 folds and tested on the remaining fold. This p ... Read More >>
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