👉 The PMC (Probabilistic Multivariate Conditional) fluid is a mathematical framework used in statistical physics and machine learning to model complex, high-dimensional data with intricate dependencies. It extends traditional multivariate distributions by incorporating conditional dependencies between variables through a graphical model structure, often represented by a Markov Random Field (MRF) or a more flexible graphical model. This framework allows for efficient inference and learning by factorizing the joint distribution into local, conditional distributions that capture the relationships between variables given their neighbors in the network. PMC fluid models are particularly useful for tasks involving spatial or temporal data, such as image processing, natural language modeling, and bioinformatics, where capturing local interactions is crucial for accurate predictions and insights.
