11e Machine Learning: Markov Chain Monte Carlo

A lecture on the basics of Markov Chain Monte Carlo for sampling posterior distributions. For many Bayesian methods we must sample to explore the posterior. Here's some basics.

[@omidgeysar7680]

The best presentation one could possibly provide for MCMC👏👏👏

[@behnamsedaghat5997]

Hi, actually I didn't get anything, could you help me with MCMC?

[@Jacobswain477]

Agreed

[@mehdirashidi3330]

You are clear, to the point, and simply effective. Thanks for being there for us!

[@roxanedebruyker2591]

Thank you very much for so clear explanations. Until here, I did not understand either MCMC or the concepts behind Bayesian linear regression and it feels way way clearer now!

[@talstol6009]

Great lecture, I had been searching for a good MCMC video for a day, and have finally found one. Thanks!

[@GeostatsGuyLectures]

Howdy Tal, I'm stoked to hear that my content is helpful! All the best, Michael

[@Alejandra-jq4xf]

Best explanation I've seen so far. You made it easy.

[@fisherpeace560]

Just found out Prof. Pyrcz is Canadian, and in my opinion, the Canadian accent is the best English accent for non-native speakers to comprehend, thanks

[@abdulmuq]

I did a whole Masters in Econometrics, and actually did quite well and hope to go back in the future for a PhD. However, I went through my whole Masters, 4 years since working in the DS industry without TRULY understanding the real concept of MCMC. Until now. Thank you so much, this was so beautifully explained!

[@amitojha3506]

Hi Michael. I am a Data Science professional.. I am recovering from Covid... I find your lectures very engaging and they have increased my interest in Bayesian Methods...

[@GeostatsGuyLectures]

I'm stoked to hear this, Amit! Thank you. All the best for a speedy recovery and for an amazing journey in Bayesian methods! Michael

[@_ali_eb2087]

thank you very much. I enjoyed your interesting presentation.

[@lewismambo8043]

Thank you very much Professor Mike. Your presentation is very clear and useful.

[@yuuniverse4444]

Thank you. Very good instructions!

[@chrispagnotta821]

Great lectures, I find these are very helpful to understand the concepts instead of getting caught up in the math details

[@ziqizhang1925]

Most clear explanation of MCMC!

[@xiaomengyuan2169]

Great video! I love the graphic illustration.

[@mikhailm2927]

Brilliant, love your work.

[@humptydumpty505]

This was awesome and very intuitive. Thanks!

[@vincentzhang3670]

It is very helpful! Thank you.

[@nksh19]

so glad that i found your channel! Thank you for all the uploads!...It would be great if there would also be video about multipoint geostats or training images! :D

[@GeostatsGuyLectures]

Check out th-cam.com/video/vd3A7RLVzA4/w-d-xo.html. Very basic, more should be added!

[@sankarshanmridha]

Very clear and concise explanation.

[@joseramonv.villatuya2108]

Great demonstration. Thanks.

[@kanshilovelace7003]

Great talk, thank you it's really helpful.

[@parthasarathimukherjee7020]

Thanks for the video, very nicely explained!

[@ShyamSunder-vs6vx]

Great! very insightful and so felicitously explained. Thanks

[@GeostatsGuyLectures]

Thank you, Shyam! I had to look up the definition for 'felicitously'! Cool!

[@ahmedabbas2595]

This is so helpful, thank you!

[@nilsfunk3875]

That was an amazing class!

[@GeostatsGuyLectures]

Thank you, Nils! I'll be adding more videos soon.

[@vincenttseng7467]

Thank you for the very clear explanation!!

[@GeostatsGuyLectures]

Thank you Vincent, I'm glad to hear that the content is useful!

[@franciszekadamski991]

Sir. I stuck with this subject for weeks. I was tying to catch the idea but was not easy for me. Sir, you have this uniqe thing to explain complex stuff. All the best for you!

[@yechiavii]

CLEAR! thank you

[@peerajak]

Thank you Prof.

[@user-dc7oj3el7o]

great explanation. thanks!

[@amirkhoutir2649]

thank you for sharing knowledge

[@Ms-RAZ]

Wonderful. Thank you!

[@mongoslade2789]

Thank you!

[@wellsitegeologist]

Thank you very much the clear explanation

[@GeostatsGuyLectures]

My goal is to make this content accessible! I'm glad to hear this!

[@filipschlembach9388]

Thank you! :)

[@dylanr8347]

amazing explanation thanks

[@BrandonSLockey]

niiiiiiice explanation dude

[@Mmmm57843]

Howdy, let me just say that your content is mesmerizing. I've been working on MCMC for seismic inversion on Python and would love to get your two cents on the subject. Also, could you talk about the no U-turn sampler?

[@delmarsunrise9765]

For Monte Carlo example (ratio of triangle area to a box area), after creating X and Y samples, how do you pair them together to check the positions in or out?

[@WahranRai]

15:00 algorithm not coherent !
the beta vector has m+1 components, in the algo you took only beta0 and beta1, why (mistake?)

[@subhajyotisaha1020]

Respected SIr, Thanks you very much for providing such lectures. Is it possible to somehow share the slides?

[@khalidifzarene8351]

👍👍

[@musiknation7218]

Do we require the Transition probability matrix for this

[@shafqat1138]

Hi Professor, are you able to explain how we calculated P(S) = 0.833 and P(R) = 0.167?
Thank you so much!

[@shamsuddeenidrisyaradua4302]

Sir I need a comprehensive lecture on Regression kriging and Poisson Kriging model

[@kwakupopsy3939]

Is this a machine learning algorithm? or can it be classified as an artificial intelligence method?

[@GeostatsGuyLectures]

Here's the definitions that I use: th-cam.com/video/9P4S0Wh4cho/w-d-xo.html. Hope this helps!

[@ghedagheda4170]

Please i want to ask you what is the different between MBK and EBK in arcgis

[@johnhausmann2391]

good content, too bad you've been using your considerable intellect to benefit big oil. Yuck. What a waste.

[@akilimali8726]

Too abstract, try to give more real life examples...