Explore >> Select a destination
|
You are here 
|
possiblywrong.wordpress.com | ||
| | | | |
jaketae.github.io
|
|
| | | | | So far on this blog, we have looked the mathematics behind distributions, most notably binomial, Poisson, and Gamma, with a little bit of exponential. These distributions are interesting in and of themselves, but their true beauty shines through when we analyze them under the light of Bayesian inference. In today's post, we first develop an intuition for conditional probabilities to derive Bayes' theorem. From there, we motivate the method of Bayesian inference as a means of understanding probability. | |
| | | | |
0fps.net
|
|
| | | | | (This is the sequel to the following post on SmoothLife. For background information go there, or read Stephan Rafler's paper on SmoothLife here.) Last time, we talked about an interesting generalization of Conway's Game of Life and walked through the details of how it was derived, and investigated some strategies for discretizing it. Today, let's... | |
| | | | |
www.oranlooney.com
|
|
| | | | | A common example of recursion is the function to calculate the \(n\)-th Fibonacci number: def naive_fib(n): if n < 2: return n else: return naive_fib(n-1) + naive_fib(n-2) This follows the mathematical definition very closely but it's performance is terrible: roughly \(\mathcal{O}(2^n)\). This is commonly patched up with dynamic programming. Specifically, either the memoization: from functools import lru_cache @lru_cache(100) def memoized_fib(n): if n < 2: return n else: return memoized_fib(n-1) + memoiz... | |
| | | | |
jamie-wong.com
|
|
| | | [AI summary] Jamie Wong discusses the tradeoffs between dynamically and statically typed programming languages, exploring how modern languages are attempting to blend the best features of both to improve iteration speed, correctness checking, and development support. | ||
