Description: Informed sampling is a sampling technique that uses prior knowledge to guide the generation process, improving efficiency. This methodology is based on the premise that by incorporating relevant and specific information about the domain of interest, samples can be selected that are more representative and useful for analysis. In the context of generative models, informed sampling allows algorithms to generate data that is more consistent with the desired characteristics, resulting in better quality outcomes. Additionally, in anomaly detection, this technique helps identify unusual patterns by focusing on areas of the data space that are more likely to contain anomalies, optimizing the detection process and reducing the number of false positives. In summary, informed sampling not only improves the quality of generated data but also optimizes the efficiency of analytical processes by directing attention to the most relevant areas of the dataset.
