Post category: Simulation
- The return of the Box-Muller method due to GPUs
- The Metropolis-Hastings algorithm in C
- The Metropolis-Hastings algorithm in Python, Julia, R, and MATLAB
- The Metropolis(-Rosenbluth-Rosenbluth-Teller-Teller)-Hastings algorithm
- Creating a reversible Markov chain using acceptance(-rejection)
- Markov goes to Monte Carlo
- The second MC in MCMC methods
- The first MC in MCMC methods
- The Metropolis-Hastings algorithm in C with multi-variable densities
- Quantum-enhanced Markov chain Monte Carlo
Post category: Machine learning
- Determinantal learning for selecting subsets in wireless networks
- Learning Theory from First Principles
- News: Accelerated PyTorch learning on Mac
- New link: Dataflowr – Deep Learning DIY
- Sparse Spiking Gradient Descent
- Spiking neural networks
- Coverage probability in wireless networks with determinantal scheduling
- Creating a simple feedforward neural network (without using libraries)
- Deep Learning: An Introduction for Applied Mathematicians
- Determinantal thinning of point processes with network learning applications
Post category: Blockchain
- Modeling and analysis of block arrival times in the Bitcoin blockchain
- A further study of some Markovian Bitcoin models from Göbel et al.
- Block arrivals in the Bitcoin blockchain
Post category: News
- Learning Theory from First Principles
- Lecture slides based on Baccelli, Błaszczyszyn, and Karray
- Quantum-enhanced Markov chain Monte Carlo
- Connectivity in device-to-device networks in Poisson-Voronoi cities
- Book manuscript: Random Measures, Point Processes, and Stochastic Geometry
- A Fields Medal goes to another percolation researcher
- News: Accelerated PyTorch learning on Mac
- Frozen code
- Coverage probability in wireless networks with determinantal scheduling
- Some remarks regarding “On the Laplace Transform of the Aggregate Discounted Claims with Markovian arrivals”
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