Comments for oscarbonilla.com http://oscarbonilla.com Sun, 22 Jan 2012 15:14:38 +0000 hourly 1 http://wordpress.org/?v=3.3.1 Comment on Visualizing Bayes’ theorem by Mark http://oscarbonilla.com/2009/05/visualizing-bayes-theorem/#comment-29611 Mark Sun, 22 Jan 2012 15:14:38 +0000 http://blog.oscarbonilla.com/?p=119#comment-29611 Outstanding! I've been trying to get my head around this for some time and your explanation made everything fall into place. Nicely done. Thanks for posting this. Outstanding! I’ve been trying to get my head around this for some time and your explanation made everything fall into place. Nicely done. Thanks for posting this.

]]> Comment on Enjoying Life (a little more) by Julissa http://oscarbonilla.com/2010/01/enjoying-life-a-little-more/#comment-29557 Julissa Thu, 15 Dec 2011 18:36:56 +0000 http://oscarbonilla.com/?p=339#comment-29557 Appericatoin for this information is over 9000-thank you! Appericatoin for this information is over 9000-thank you!

]]> Comment on Visualizing Bayes’ theorem by Hemant Patel http://oscarbonilla.com/2009/05/visualizing-bayes-theorem/#comment-29548 Hemant Patel Thu, 08 Dec 2011 21:27:52 +0000 http://blog.oscarbonilla.com/?p=119#comment-29548 Very usefull article Wiki page must have link of Such a good and easy to under stand article. Thank you. Very usefull article

Wiki page must have link of Such a good and easy to under stand article.

Thank you.

]]> Comment on Visualizing Bayes’ theorem by freethoughtful http://oscarbonilla.com/2009/05/visualizing-bayes-theorem/#comment-29536 freethoughtful Sat, 26 Nov 2011 05:41:32 +0000 http://blog.oscarbonilla.com/?p=119#comment-29536 Nicely done. A similar (and equally intuitive and effective) visual explanation of conditional probability and Bayes theorem based on Venn diagrams can be found in Bolstad's textbook on Bayesian Statistics. Nicely done. A similar (and equally intuitive and effective) visual explanation of conditional probability and Bayes theorem based on Venn diagrams can be found in Bolstad’s textbook on Bayesian Statistics.

]]> Comment on Visualizing Bayes’ theorem by E. Fitzgerald http://oscarbonilla.com/2009/05/visualizing-bayes-theorem/#comment-29530 E. Fitzgerald Mon, 14 Nov 2011 23:33:48 +0000 http://blog.oscarbonilla.com/?p=119#comment-29530 Dear Oscar, A clear presentation - a pleasure to read... Dear Oscar,

A clear presentation – a pleasure to read…

]]> Comment on Visualizing Bayes’ theorem by Ann http://oscarbonilla.com/2009/05/visualizing-bayes-theorem/#comment-29516 Ann Tue, 01 Nov 2011 07:24:31 +0000 http://blog.oscarbonilla.com/?p=119#comment-29516 very useful. thank you very useful. thank you

]]> Comment on Visualizing Bayes’ theorem by ob http://oscarbonilla.com/2009/05/visualizing-bayes-theorem/#comment-29511 ob Thu, 27 Oct 2011 18:46:54 +0000 http://blog.oscarbonilla.com/?p=119#comment-29511 I didn't want to overcomplicate the example. I didn’t want to overcomplicate the example.

]]> Comment on Visualizing Bayes’ theorem by ob http://oscarbonilla.com/2009/05/visualizing-bayes-theorem/#comment-29510 ob Thu, 27 Oct 2011 18:46:24 +0000 http://blog.oscarbonilla.com/?p=119#comment-29510 80% of women with breast cancer will get a positive mammogram means that P(positive mammogram | woman has cancer) = 0.8. The remaining 20% is the probability of getting a negative mammogram given that the woman has cancer. The 9.6 comes from the other part of the population, i.e. women without cancer that also get a positive mammogram. In general, P(B | A) + P(¬B | A) will sum to 1, but P(B|A) + P(B|¬A) will not as they are unrelated probabilities. 80% of women with breast cancer will get a positive mammogram means that P(positive mammogram | woman has cancer) = 0.8.
The remaining 20% is the probability of getting a negative mammogram given that the woman has cancer.

The 9.6 comes from the other part of the population, i.e. women without cancer that also get a positive mammogram.

In general, P(B | A) + P(¬B | A) will sum to 1, but P(B|A) + P(B|¬A) will not as they are unrelated probabilities.

]]> Comment on Visualizing Bayes’ theorem by ob http://oscarbonilla.com/2009/05/visualizing-bayes-theorem/#comment-29509 ob Thu, 27 Oct 2011 18:42:41 +0000 http://blog.oscarbonilla.com/?p=119#comment-29509 P(B) is the total probability of B, it's the chance of B happening regardless of whether A happens. Thus, P(B) = P(B|A)P(A) + P(B|¬A)P(¬A). P(B) is the total probability of B, it’s the chance of B happening regardless of whether A happens. Thus, P(B) = P(B|A)P(A) + P(B|¬A)P(¬A).

]]> Comment on Visualizing Bayes’ theorem by Mary http://oscarbonilla.com/2009/05/visualizing-bayes-theorem/#comment-29506 Mary Sat, 22 Oct 2011 18:57:01 +0000 http://blog.oscarbonilla.com/?p=119#comment-29506 I agree, I can't understand how to figure out Pr (B) :( I've got a stats module in my masters and having come from a non mathematical background... I'm struggling a bit. I agree, I can’t understand how to figure out Pr (B) :(

I’ve got a stats module in my masters and having come from a non mathematical background… I’m struggling a bit.

]]> Comment on Visualizing Bayes’ theorem by Ganesh Krishnan http://oscarbonilla.com/2009/05/visualizing-bayes-theorem/#comment-29505 Ganesh Krishnan Sat, 22 Oct 2011 03:46:53 +0000 http://blog.oscarbonilla.com/?p=119#comment-29505 >80% of women with breast cancer will get positive mammograms. 9.6% of women >without breast cancer will also get positive mammograms. 80% have cancer and 9.6% don't have cancer (when you have positive mammogram). Why doesn't 80 + 9.6 addup to 100%? What probabilities are remaining? Damn! I wish I had slept in my math classes >80% of women with breast cancer will get positive mammograms. 9.6% of women
>without breast cancer will also get positive mammograms.

80% have cancer and 9.6% don’t have cancer (when you have positive mammogram). Why doesn’t 80 + 9.6 addup to 100%? What probabilities are remaining?

Damn! I wish I had slept in my math classes

]]> Comment on Visualizing Bayes’ theorem by luis http://oscarbonilla.com/2009/05/visualizing-bayes-theorem/#comment-29504 luis Thu, 20 Oct 2011 18:43:09 +0000 http://blog.oscarbonilla.com/?p=119#comment-29504 I think your explanation is flawed. You said P(A) = | A | / |U|, but that is only true if all elements of A have the same probability. In general that is a false statement. I think your explanation is flawed. You said P(A) = | A | / |U|, but that is only true if all elements of A have the same probability. In general that is a false statement.

]]> Comment on Visualizing Bayes’ theorem by Visualizing Bayes’s theorem | The Personal Blog of Artem Koval, M.Sc. http://oscarbonilla.com/2009/05/visualizing-bayes-theorem/#comment-29503 Visualizing Bayes’s theorem | The Personal Blog of Artem Koval, M.Sc. Thu, 20 Oct 2011 17:45:27 +0000 http://blog.oscarbonilla.com/?p=119#comment-29503 [...] an incredible article! I wish I had this explanation in the high school. As a fully qualified mathematician I want to say [...] [...] an incredible article! I wish I had this explanation in the high school. As a fully qualified mathematician I want to say [...]

]]> Comment on Compiladores by ADC http://oscarbonilla.com/compiladores/#comment-29496 ADC Tue, 11 Oct 2011 08:30:38 +0000 http://oscarbonilla.com/?page_id=225#comment-29496 Buenas, algunos de los PowerPoint estan corruptos, ya que al abrir-los contienen página en blanco. Por lo demás todo perfecto. Saludos Buenas, algunos de los PowerPoint estan corruptos, ya que al abrir-los contienen página en blanco. Por lo demás todo perfecto.

Saludos

]]> Comment on Radiation by AP http://oscarbonilla.com/2011/04/radiation/#comment-29484 AP Sun, 25 Sep 2011 07:46:17 +0000 http://oscarbonilla.com/?p=854#comment-29484 Thanks for the fantastic primer! Thanks for the fantastic primer!

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