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Probability in Artificial Intelligence: Exploring Joint, Marginal, and Conditional Probabilities +91 +91- -9989971070 9989971070 www.visualpath.in www.visualpath.in
Introduction Welcome to the presentation on Probability in Artificial Intelligence. Today, we'll delve into the concepts of joint, marginal, and conditional probabilities and their significance in AI. www.visualpath.in
Overview of Probability Theory Probability framework to model uncertainty and make informed decisions. It is fundamental to various AI tasks such as machine learning, probabilistic reasoning, and decision-making under uncertainty. theory provides a www.visualpath.in
Joint Probability Joint probability refers to the likelihood of multiple events occurring simultaneously. In AI, it's crucial for modeling complex relationships between variables in probabilistic graphical models (PGMs). www.visualpath.in
Example of Joint Probability Illustration: network representing the relationship between weather conditions, traffic, and arrival time. Joint probabilities quantify the likelihood of specific combinations of events. Consider a Bayesian www.visualpath.in
Marginal Probability Marginal probability focuses on the probability of individual events without considering other variables. It's derived from joint probabilities through marginalization, essential for various AI tasks including classification and clustering. www.visualpath.in
Example of Marginal Probability Illustration: Using the same Bayesian network example, probabilities provide insights into the likelihood of conditions regardless of traffic or arrival time. marginal specific weather www.visualpath.in
Conditional Probability Conditional probability measures the likelihood of an event occurring given that another event has already occurred. It's fundamental for relationships and making predictions based on observed evidence. modeling cause-effect www.visualpath.in
Example of Conditional Probability Illustration: Bayesian probabilities enable predicting traffic congestion given specific weather conditions. Continuing network, with conditional the www.visualpath.in
Applications in AI Probability theory, with its concepts of joint, marginal, and conditional probabilities, is applied across various AI domains. Examples include machine learning algorithms, probabilistic graphical models, and decision- making under uncertainty. www.visualpath.in
Conclusion Probability theory is indispensable in artificial intelligence for modeling uncertainty, making decisions, and building intelligent systems. Understanding joint, marginal, and conditional probabilities is crucial for advancing AI capabilities across diverse applications. www.visualpath.in
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