Thanks for this video. It makes the concept very clear. Other videos, not so much. I have an application where I would like to use 2D KDE on data sets that are set of point on an xy plane. My goal is to fit a 2D Gaussian to the data and then compare goodness of fit for different data sets. I believe I first need to generate a density function for the data and then fit the Gaussian to the density function. KDE looks like a good way to generate the density function. I would prefer to do this in Excel so an Excel plugin would be ideal. I am not really setup (or proficient) to do regular programming in Python, C, or whatever.
Nice video! I have some suggestion for improvement: I think you should also consider which moves a Pokemon can learn, the power of each move, which stat (attack / special) will be used to attack with the move and STAB. It seems that Gengar is the best Pokemon in the current best team (highest edge), but it does not have any good Ghost moves and no Poison moves in Gen 1, and in Gen 1 - 3, Ghost and Poison are physical moves while Gengar has a very low attack stat (65) and very high special (130) stat, so it would probably be better if it uses different type moves. This would likely also increase the potential of pokemon with very good base stats and very good moves but a bad type (e.g. Snorlax). To determine the edge, instead of only considering type advantages, maybe you can calculate the maximum damage a our Pokemon can do to the opponent Pokemon (something like move power * attacking stat / defending stat * STAB * defending type effectiveness * accuracy (accuracy is not part of damage calculation, but it will give a result closer to the expected value)) which I will call max_attacking_edge, and the maximum damage the opponent pokemon can do to our pokemon which i will call max_defending_edge, and then edge = max_attacking_edge - max_defending_edge. I think it is fine if you ignote secondary effects and status moves.
Title is great - but might want to add "- Monte Carlo simulation and computation-based statistics for programmers" to give people an idea what it is about?
finally, i found an amazing lecture on kernel density estimation thanks a lot . but i have one query how it can be used to find the anomaly detection. sir can u please make one lecture about this topic otherwise can u please recommand me some good references for KERENEL DENSITY ESTIMATION FOR ANOMALY DETECTION
Hello there. I tried using your KDE package for my work. Used FFT KDE. When i was trying to evaluate the model with some data-i got an error-'Every data point must be inside the grid" . could you elaborate on this,please?
If you have a data point at 0, say, and you grid ranges from 1 to 5, then you will get this error. The data point is outside of the grid. Best to let KDEpy create the grid for you. It automatically sets up a reasonable grid.
