A research-grade equity trading bot that uses a Hidden Markov Model to identify market regimes in real time and adjusts position sizing continuously using the Moreira-Muir (2017) volatility-targeting ...

We investigate the transition processes between the emitting (ON) and non-emitting (OFF) states of fluorescent molecules using a machine-learning approach. In fluorescently labeled DNA, continuous ...

The central challenge in portfolio management is not predicting future returns but rather adapting to changing market conditions. This article presents a systematic approach to tactical asset ...

Graphical representations model complex networks by encoding entities as vertices and interactions as edges, with recurring subgraphs—or motifs—revealing fundamental organizational principles. We ...

A complete C++ implementation of the Python hmmlearn library, featuring modern C++17, Eigen for linear algebra, and comprehensive HMM algorithms. hmm_c++/ ├── include/ # Header files │ ├── types.hpp # ...

Here’s a test for you: Visit Amazon or any other site selling books, search for a random title, and see how long it takes for an obviously AI-generated entry to appear. Browse the recommended titles ...

In our initial research phase, Omar Tazi and I established a probabilistic forecasting framework, utilizing Bayesian Networks to map dependencies between oil price movements and key macroeconomic ...

Individuals in the midst of a mental health crisis frequently exhibit instability and face an elevated risk of recurring crises in the subsequent weeks, which underscores the importance of timely ...

Abstract: We consider system identification (learning) problems for Gaussian hidden Markov models (GHMMs). We propose an algorithm to tackle the cases where the data is recorded in aggregate ...

In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer ...