Monte Carlo Methods - VISUALLY EXPLAINED!

In this tutorial, I provide all the necessary background on how to use sampling methods to estimate the distributions and compute expected values.

I provide an overview of 3 sampling methods as well -
a) Inverse CDF Transform
b) Rejection Sampling
c) Importance Sampling

As such I have dedicated tutorials on these methods so if you need more in-depth explanations & various proofs then watch the stand-alone dedicated tutorials on these methods.

[mathewjones]

Thank you, again. I use Markov chains and Monte Carlo methods to model ion channel function and other things. I was foolish enough to believe that I had independently figured out what you describe as the Inverse CDF Transform. It is actually a relief to learn that there are already established principled theorems about this. So, maybe some of my publications were not barking up the wrong tree after all :-).

[laurentoliviercoderre]

Thank you for your work ! Coming from a physics and computer science background, it totally helps bridging the gap to statistics !

[bikinibottom]

10:50 you wouldn't be wasting our time, this video about generating random numbers seems very interesting!

[bikinibottom]

This Bayesian regression serie is incredible, the way it starts with a polynomial regression and then builds up to more general probabilistic modelling is brilliant and reminds me of Rasmussen's book on Gaussian processes. Are you going to make a video about kernels? That would nicely generalize the cosine example of the part 1

[NasimBinesh]

Hi there! Thank you for this insightful video. How can we determine the best sampling method? I have a problem gambling study that uses social media posts to predict the probability of users moving from one state to another (e.g., addiction -> seeking help) thank you!

[Matteo-uqgc]

Great Video! I subscribed please keep them coming !!!

[bharadwajreddy]

waiting for kernels and Gaussian processes. All of your videos are amazing:)

[rileskebaili]

For when a video about the theoretical aspect of score based diffusion models ?

[paedrufernando]

I think importance sampling in Reinformcaent Learning..and this large time number of sampling represents the large number of trails we train the Actors and Critics for. Is that so or I am mistakendly confusing topics