Prior and Posterior Probabilities in Bayesian Networks

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This short video tutorial explains the difference between prior and posterior probabilities in Bayesian networks. The explanation is made using a simple example.
  1. Dr. Zaman Sajid
  2. Bayesian network
  3. influence diagram
  4. probabilistic analysis
  5. decision making
  6. decision network
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Комментарии
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I've searched through a wide range of videos, by far this is the best and most straightforward video of the bunch.
Thank you so much for posting it!

thelambsauce
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I have been looking for this peace of knowledge for quate sometime now, come across with it it has real served. Thank you.

GideonRwabona
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This explanation was thorough and concise. Thank you sir.

VectorSpace
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I spent a few hours on my pattern recognition book to understand this, I could not this video really clear everything thanks

ibrahimkouma
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Hello Sir, Your videos are really great; I have much appreciation and a lot of respect for you. I am learning GeNie by using your video. My data set contains both discrete and continuous variables at the same time. When I try to run my model using a PC algorithm, it says it does not support it. Can you please guide me here?

SaneInsaneEuropeWithHasan
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Great video!!! Thanks for posting this! I have a question though: for the weather example, if we have "observations" for the probability of raining or not given today is a cloudy day, could we define P(Cloud) as the prior probability and P(Could|Rain) as the posterior probability? To ask it in a more general way, does the definition of prior/posterior probability depend on the nature of the problem or the information/observation we are given?

peihongyu
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Where is the heck "Bayesian Network"?!!

scorpion
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@drzamansajid Sir is this your email address that mentioned in video?
I emailed you if that's your email address

rafayaftab