This short video tutorial explains the difference between prior and posterior probabilities in Bayesian networks. The explanation is made using a simple example.
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?
Sorry for the late reply. Somehow, I overlooked your message. I am not sure what you mean by the nature of the problem, but yes, posterior probability is based on new information we have about the system. I am unsure if this answers your question, but that is the best I could understand your question.
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?
Thank you. I am glad to know it is helpful for you. Yes, you are right. GeNie does not support a mixture of discrete and continuous variables. What you can do is discretize your continuous variables, and in that way, GenNie can run the algorithm or you may use other software such as BayesFusion or AgenaRisk. I hope this helps.
This video is on prior and posterior probabilities used in the Bayesian Network. For Bayesian Network, you can watch ua-cam.com/video/l_npNK-LbZ4/v-deo.html if you are interested to learn how to use a tool for BN modelling watch here ua-cam.com/video/d6nbgqM8jnc/v-deo.html
I have been looking for this peace of knowledge for quate sometime now, come across with it it has real served. Thank you.
This explanation was thorough and concise. Thank you sir.
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!
I spent a few hours on my pattern recognition book to understand this , I could not this video really clear everything thanks
I am glad it helped. Let me know if any questions. Good Luck!
Thank you. It is a nice explanation.
good presentation and explaination
Thank you for explanation
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?
Sorry for the late reply. Somehow, I overlooked your message. I am not sure what you mean by the nature of the problem, but yes, posterior probability is based on new information we have about the system. I am unsure if this answers your question, but that is the best I could understand your question.
Thanks!
Thank you!
thank a lot
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?
Thank you. I am glad to know it is helpful for you. Yes, you are right. GeNie does not support a mixture of discrete and continuous variables. What you can do is discretize your continuous variables, and in that way, GenNie can run the algorithm or you may use other software such as BayesFusion or AgenaRisk. I hope this helps.
Where is the heck "Bayesian Network"?!!
This video is on prior and posterior probabilities used in the Bayesian Network. For Bayesian Network, you can watch ua-cam.com/video/l_npNK-LbZ4/v-deo.html if you are interested to learn how to use a tool for BN modelling watch here ua-cam.com/video/d6nbgqM8jnc/v-deo.html
@drzamansajid Sir is this your email address that mentioned in video?
I emailed you if that's your email address