Artificial Intelligence Courses Online | AI Online Training

1
Probability in Artificial Intelligence
Exploring Joint, Marginal, and Conditional
Probabilities
91-9989971070
www.visualpath.in
2
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
3
Overview of Probability Theory

•  
• Probability theory provides a 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.

www.visualpath.in
4
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
5
Example of Joint Probability

• Illustration Consider a Bayesian network
  representing the relationship between weather
  conditions, traffic, and arrival time. Joint
  probabilities quantify the likelihood of specific
  combinations of events.

www.visualpath.in
6
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
7
Example of Marginal Probability

• Illustration Using the same Bayesian network
  example, marginal probabilities provide insights
  into the likelihood of specific weather
  conditions regardless of traffic or arrival time.

www.visualpath.in
8
Conditional Probability

•  
• Conditional probability measures the likelihood
  of an event occurring given that another event
  has already occurred.
• It's fundamental for modeling cause-effect
  relationships and making predictions based on
  observed evidence.

www.visualpath.in
9
Example of Conditional Probability

• Illustration Continuing with the Bayesian
  network, conditional probabilities enable
  predicting traffic congestion given specific
  weather conditions.

www.visualpath.in
10
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
11
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
12
CONTACT
For More Information About Artificial
Intelligence Training Address- Flat no
205, 2nd Floor
Nilgiri Block, Aditya
Enclave, Ameerpet, Hyderabad-16 Ph No
91-9989971070 Visit www.visualpath.in
E-Mail online_at_visualpath.in
13
THANK YOU
Visit www.visualpath.in