Decision Analysis 5: Posterior (Revised) Probability Calculations
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- Опубликовано: 27 окт 2024
- Calculating Posterior Probabilities for Decision Trees
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You have no idea how you just saved my life Joshua. Great help and simplified explanations. Thanks so much.
I just want to say thanks Joshua. Your material has been of great help. God Bless You
+Moses Sei
You're welcome Moses.
Joshua Emmanuel - you are awesome!! Thank you so much for simplifying this concept. You have blessed me tremendously. I was going crosseyed trying to understand this principle when rearranged mathematically in a way that was more difficult for me to conceptualize. Thank you!
Omg!! You just save my whole semester in just a 3-minute video! thanks a lot!
Best explanation of Bayesian Posteriors I've seen so far!
Finally a Bayes Classification explanation I can actually understand. Thank you !
Amazing video. I've watched at least 5 others & didn't understand, but the way you used tables & explained everything both with words & visuals made it so easy to follow. Thanks for the help!
Joshua, Thanks Man ! so much better than university's lectures !
Ohad S
You're welcome Ohad.
Waaay better...❤️Thankyou
I 100% agree!
It is described as simple, short and nice way to understand the concept clearly. Material and presentations are too good. Thank you for sharing.....
Wow, thankyou so much. I am 4th year engineer and this is going to help so much with my test tomorrow on Decision Theory
You really have a gift for teaching! Thank you!
This is one of the best explanations of this topic I've seen. Awesome!
Thank you so much Joshua Sir. Your explanation on posterior probability saved me.I was able to resolve my issue.
Glad it helped, Lopamudra.
Awesome! A very simplified presentation of a complex concept.
thanks so much, man. you teach in a way that's so much easy to understand. appreciate it
Thank you for your online video presented in clear way.
🙏
Joshu,the man with brain 💥💥💥
Love you brother ❣️❣️❣️❣️
Thanks for the instructions on how to make a decision tree. Honestly, this really helped me in the final stage of my final project.. thankyou so much.. 😇😇
Absolutely love your explanation and the table format was super helpful
Thank you so much!
Thank you so much for the video Joshua! My stats teacher is a complete bozzo so thanks for saving my grade
Thank you for the excellent concise lecture. I am wondering how I should conclude or interpret the analysis?
hey Joshua, thank you so much for this Video mate! due to this video i will be able to score high in my exam. more power to you!!!
You explained in 4 minutes what hours of lectures couldn't
Amazing tutorial! Concise and precise! Thanks for making this video!
Thanks Joshua! You make this material easy to understand and really helpfull.
GBU
I subscribed immediately. That was clear!
OMG 😍😍🙏🏾🙏🏾Thank you so much Joshua. Please, can you show us how we draw the decision tree after determining the posterior probabilities?
See if this helps:
ruclips.net/video/FUY07dvaUuE/видео.html
I watched this video but I am not able to apply it to my exercise. I have many qustions. can you help me?
Please do a Bayes Theorem Video! Love your content!
You explained in very simple manner
thank you so much!! it helps me lot for tomorrow exam!!
This was very well explained, thank you !
These are great videos. Thank you so much!!
This video was amazing!!! Thank you so much!
Hello Mr. Joshua. Can you kindly explain the value of information. That is the Market value, cost value and Economic value? This is related to the new infonomics trend.
Great video and helpfull
lmfao i made the exact same mistake you mentioned by trying to do this before you explained it all hahah love this video
Excellent material big thanks
very good presentation, thank you so much :)
Wow thanks for this clear video!!
Helpful one.
Thank you!! You really made it easy to understand :)
Thank you so much, I could understand the concept!!!
Simplified teaching.
Hello Joshua... Thnx a lot... Its helpful... But l want to inquire about how to make a decision given data to calculate the posterior probabilities... How do u conclude from the posterior probs concerning a certain problem at hand...
Thank you so much
Excellent. Can you please explain the interpretation of the posteriori to a decision.
posteriori refers to "later" or "after"
In essence, the additional information obtained after the decision is made.
final exams twos day later... thank you for saving my life
thank you your the best
Joshua,what if the question wrote the other way around.For example "when the consultant's report was positive 65%,the economic was growth".So the (Growth | Positive)= 0.65 and (Decline | Positive ) = 0.35.Am I right?please answer my question cause i am confuse with the question when they change the place of the word "given" which is consider as "when" in this question.
Correct. You can replace "when" with "given".
Thank you!
Joshua excellent videos, by any chance do you have a video about Decision Making under risk , sensitivity analysis ?
Sorry, I have nothing on Sensitivity in Decision Analysis.
Awesome question, have you found any answers since 1 year?
If we never had a consultant, so we have no historical data about their performance. How should we assign the probabilities of negative and positive report?
Unfortunately, we cannot assign those probabilities unless provided. We will just stick with the prior probabilities in making the decision.
Thanks again! you saved me
thank you so much for this!!
Love the stuff. Super helpful for reviewing for a big stats exam comin up!
Thank you so much!!
thanks a lot ....
I have a couple 2 examples about decision making with probability, could you please solve it for me, Thank you
Thank you!!
You and a friend are playing toss the coin and the coin are tossed twice. Each of you get a turn to call heads or tails to indicate the manner the coin would land after it has been tossed. You are required to:
a. Compute the probability that a head would result on the first toss.
b.Compute the probability that a tail would result on the second toss given that the first toss resulted in a head.
c. Compute the probability that two tails would result
d. Compute the probability that a tail would result on the first toss and a head on the second toss.
e. Compute the probability that a tail would result on the first toss and a head on the second toss or a head on the first and a tail on the second toss. Elaborate your answer
f. Compute the probability that at least one head would occur on the two tosses. Elaborate your answer
Khanyisani Mlaba
Check out the solutions here: ruclips.net/video/6HppFWelx64/видео.html
Hello @Joshua, Do you do coaching?
Sure. Please see About section of this channel for contact info.
thanks man
Is conditional probabilities should equal to 1 or it can more or less?
It can be more or less.
Why is this example so easy compared to my professor's exam questions. His questions can not be solved at all.
tanks so much sır.
ı rlly apprecıate for dıs vıdeos
Is this the same as calculating Bayes Probability or am i missing something
Yes, it is the same...just in table format.
Is it necessary to get 1 or it's okay to get .99?
The posterior probabilities normally should add up to 1. If they don't, then check your rounding or increase the number of decimal places you round to. I will suggest rounding intermediate results to 4 decimal places.
Prior probability. Conditional
P(E1)=0.10. 0.4
P(E2)=0.70. 0.7
P(E3)=0.20. 0.5
How this will be calculated is not happening to me.
How many conditions do you have?
thank man
Can the marginal probability not equal to 1?
The sum of the marginal probabilities must equal 1.
I lovvvvve youu
Why don't the conditional probabilities add up to 1?
Conditional probabilities don't add up to 1 because they are not complementary.
how ca i get it
What is your email?
thank you!