دانلود کتاب برنامه‌نویسی احتمالی پایتون با PyMC و ArviZ

Probabilistic programming is an exciting and transformative approach that merges programming with the principles of probability theory, allowing us to model uncertainty in a structured and powerful way. At its essence, probabilistic programming equips us with the tools needed to create models that can handle the unpredictability of real-world situations. This is particularly valuable in fields such as data science, machine learning, artificial intelligence, and decision-making, where uncertainty is a constant factor. To understand probabilistic programming, we first need to grasp the concept of probability itself. Probability quantifies how likely an event is to occur, ranging from 0 (impossible) to 1 (certain). In many real-life scenarios, outcomes are uncertain. For instance, if you flip a coin, you can’t predict with absolute certainty whether it will land on heads or tails. However, you can say there is a 50% chance for each outcome. Probabilistic programming takes this idea further by allowing us to express complex relationships and uncertainties in a computational format. Instead of just running deterministic algorithms that yield a single outcome based on given inputs, probabilistic programming enables us to create models that accommodate variability and uncertainty.