Why Ising model : 3 reasons for relevance Studying Ising model can be useful to understand phase transition of various systems. Hopfield network or Boltzmann machine to the neural network is just a generalized form of Ising model. Ising model is also useful as a statistical model in its own right. Ising model $\boldsymbol{x}$ is the state of an Ising model with $N$ spins be a vector in which each component $\boldsymbol x_n$ takes values $-1$ or $+1$.

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Efficient Monte Carlo sampling This post is on the extension of the post about Hamiltonian Monte Carlo method. Therefore, I assume the readers already read the post. Overrelaxation also reduces the random property of the Monte Carlo sampling, and speeds up the convergence of the Markov chain. Gibbs sampling In advance of studying over relaxation, we study Gibbs sampling. In the general case of a system with K variables, a single iteration involves sampling one parameter at a time.

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Namshik Kim

physicist, data scientist

Data Scientist

Vancouver, BC, Canada.