The Angry Statistician

The Higgs boson is the smallest detectable wave in the Higgs field . Interacting with the Higgs field causes  articles to acquire inertial mass; without the Higgs field, no particle would have inertial mass. Some particles don't feel the Higgs field at all ( photons ) and so are massless; some feel it very lightly ( neutrinos ) and have little mass; ordinary particles feel it strongly. In physics the current best understanding of the forces (excluding gravity) is called the Standard Model . The one remaining elementary particle in the Standard Model that hasn't been experimentally detected is - the Higgs boson. The Standard Model describes these forces: Electromagnetism (attraction/repulsion due to electric charge) Weak force (causes radioactive decay) Strong force (holds quarks together to form protons,neutrons) Electromagnetism is the unification of electricity and magnetism, which were originally thought to be two different forces. The next steps in phys...

Quantum Mechanics and Quantum Computation About the Course Quantum computation is a remarkable subject, and is based on one of the great computational discoveries that computers based on quantum mechanics are exponentially powerful. This course aims to make this cutting-edge material broadly accessible to undergraduate students, including computer science majors who do not have any prior exposure to quantum mechanics. The course will introduce qubits (or quantum bits) and quantum gates, the basic building blocks of quantum computers. It will cover the fundamentals of quantum algorithms, including the quantum fourier transform, period finding, and Shor's iconic quantum algorithm for factoring integers efficiently. The course will also explore the prospects for quantum algorithms for NP-complete problems and basic quantum cryptography. The course will not assume any prior background in quantum mechanics. Instead, it will use the language of qubits and quantum gates ...

Dive Into Python Google's Python Class Learn Python The Hard Way

I've created a GitHub repository for some fun simulations and other code to illustrate ideas and applications. I  believe it's helpful for many people to use simulations to better understand what's going on when learning statistics. Why are things done the way they are? Well, let's simulate the random process and find out! These are currently in Python but I'll be adding R versions. I'll be adding simulations that illustrate particular ideas in probability and statistics, or that are just fun. Some of the most interesting and useful, I believe, will be related to hypothesis testing. The initial repository has a basic Monty Hall simulation and two roulette simulations that sample from either a uniform or exponential distribution. I tend to use NumPy quite a bit. sim-udacity on GitHub