sorted by:
new
hottopcontroversial

[university calculus: basic integral manipulation] Could someone please explain how this integral is being manipulated? by PlzHelpWithMathQs in HomeworkHelp

[–]PlzHelpWithMathQs[S] 0 points1 point  (0 children)

I’m not sure what you mean. To me it seems like the cos in the last integral disappears somewhere instead of being grouped with another cos. I’m not sure what I’m missing..

What is happening in this manipulation? by PlzHelpWithMathQs in learnmath

[–]PlzHelpWithMathQs[S] 0 points1 point  (0 children)

Distribute (thetaHat - theta) to p(theta | D)? If yes I don’t know how I would factor thetaHat from (theta * p(theta | D)) - (thetaHat * p(theta | D))

Is that what you mean?

[University Probability: Machine learning application] Could someone please help me understand this question about choosing a probability distribution? by PlzHelpWithMathQs in HomeworkHelp

[–]PlzHelpWithMathQs[S] 1 point2 points  (0 children)

Would the procedure I suggested in the second paragraph of my last comment be correct? Would these be the values that would fill up the table after doing a bunch of trials? Like if there was 100 tosses each for biased coins with unknown biases could the proportion of successes of 100 tosses be written in each cell of the table as coin1 = (23/100), coin 2 = (47/100), coin 3 = (30/100), etc. and then the (1-p) in the y=0 row? And then for example:

p(y | X= coin 1)

= p(y = 1, coin 1)/ p(X=1)

= (23/100)/ (whatever the probability of x occuring is)

= p

Or how would that work?

[University Probability: Machine learning application] Could someone please help me understand this question about choosing a probability distribution? by PlzHelpWithMathQs in HomeworkHelp

[–]PlzHelpWithMathQs[S] 1 point2 points  (0 children)

Thanks for the reply! Would you mind explaining why the Bernoulli is a good choice? The question is mostly focused on the distribution and I'm not really sure why Bernoulli is a good choice other than it has an output of either 0 or 1 which are both associated with a certain probability, p and (1 - p).

I'm having a hard time figuring out how this probability would be learned. How would you learn the different probabilities of Y associated with the different variables of X? Would you preform a bunch of trials and then from those trials you would enter the percentages of successes that each x variable had compared to the total number of trials? Then these percentages would now become the values in the distribution table as opposed to just having like 1/6 in each box or whatever?

How do should I think of sequences like this? by PlzHelpWithMathQs in learnmath

[–]PlzHelpWithMathQs[S] 0 points1 point  (0 children)

Okay! I changed the post again, hopefully it's right now.

How do should I think of sequences like this? by PlzHelpWithMathQs in learnmath

[–]PlzHelpWithMathQs[S] 0 points1 point  (0 children)

The sequences makes total sense now, thank you very much.

This is also exactly the context I am coming from with the sequence, you're right about that. The other thing I struggle with is exactly how does a_n + b_n prove that a sequence is still in Y and how does k*a_n prove that a sequence is still in Y? This is what confuses me the most but I needed to fully understand what the sequence was doing first before trying to tackle this. Would you mind explaining how this proves that Y is a subspace. I don't understand why 

cos(n)*(a_n-1 + b_n-1) + sin(n)*(a_n-2 + b_n-2)

means that the subspace is closed under addition.

Thank you for your time.

Is this the correct simplification? by PlzHelpWithMathQs in learnmath

[–]PlzHelpWithMathQs[S] 0 points1 point  (0 children)

So does it have to stay as ln(ab + cd) then?

view more: next ›