Probabilistic models are a type of statistical model that use probabilities and statistical data to make predictions and understand complex relationships in data. These models are used in a variety of fields, including natural language processing, computer vision, and machine learning, to name a few. In NLP, probabilistic models are used to analyze and understand the relationships between words and phrases in text. In computer vision, probabilistic models are used to model the distribution of image features, such as edges and shapes.
The goal of probabilistic models is to quantify the uncertainty in the data and to use this information to make predictions. They are used to model complex relationships and interactions between variables, and to estimate probabilities of certain events happening based on past data.
Probabilistic models are powerful tools for data analysis and prediction, but they can also be computationally expensive and require careful modeling and tuning to produce accurate results. In this chapter, we will explore the fundamentals of probabilistic models, their applications, and the algorithms used to implement them. We will also examine various techniques for model selection, validation, and optimization, as well as some of the most common pitfalls and challenges in using probabilistic models.