To address these challenges, we propose a density-based probabilistic graphical model (DB-PGM) to achieve adaptive multi-target encirclement. DB-PGM models the cooperative encirclement of multiple ...

Probabilistic Graphical Models (PGMs), which provide compact graphical representations of variable distributions, offer a complementary approach with well-developed methods for capturing relationships ...

No previous knowledge of pattern recognition or machine learning concepts is assumed. This is the first machine learning textbook to include a comprehensive coverage of recent developments such as ...

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...