Description: Multimodal Outlier Analysis Models are statistical approaches that focus on identifying and analyzing data that significantly deviates from the norm in datasets that exhibit multiple modes or distributions. These models are particularly useful in contexts where data do not follow a normal distribution, which is common in many real-world applications. Through techniques such as mixture distributions, these models allow for the detection of complex patterns and variations in the data, facilitating the identification of outliers that may indicate interesting phenomena or errors in data collection. The ability to handle multiple modes in data is crucial, as many real datasets exhibit heterogeneous characteristics that cannot be adequately captured by unidimensional models. In this sense, Multimodal Models provide a richer and more nuanced perspective, enabling analysts to gain a deeper understanding of the underlying structure of the data and, therefore, make more informed decisions based on the precise identification of outliers.
