Practical Probabilistic Programming: Volume 2
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Probabilistic programming combines probability with computer programming to handle uncertain situations. It involves creating models that include random variables and events, which helps in predicting outcomes when there is uncertainty. Using probabilistic programming languages like PyMC3 and Stan, you can define complex models that represent real-world problems. These programs make it easier to perform inference, which means making predictions or decisions based on the models. This approach is useful in many fields, including machine learning, artificial intelligence and risk management, wher...
Probabilistic programming combines probability with computer programming to handle uncertain situations. It involves creating models that include random variables and events, which helps in predicting outcomes when there is uncertainty. Using probabilistic programming languages like PyMC3 and Stan, you can define complex models that represent real-world problems. These programs make it easier to perform inference, which means making predictions or decisions based on the models. This approach is useful in many fields, including machine learning, artificial intelligence and risk management, where it's important to work with incomplete or uncertain data. Probabilistic programming helps in understanding and managing risks, making better decisions and improving the accuracy of predictions in various applications. This book elucidates the concepts and innovative models around prospective developments with respect to computer and information science. Most of the topics introduced in this book cover new techniques and the applications of probabilistic programming. This book is a complete source of knowledge on the present status of this important field.
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