Abstract: Existing algorithms for estimating the model parameters of an explicit-duration hidden Markov model (HMM) usually require computations as large as O((MD/sup 2/ + M/sup 2/)T) or O(M/sup 2/ DT ...

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 ...

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 ...

In Part 1, we explored the fundamentals of Hidden Markov Models and how to fit them to financial returns data. We ended with the plot_in_sample_hidden_states function that visualizes how the HMM ...

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 ...

‡ Institute of Applied Physics, Center for Functional Nanostructures, and Institute of Toxicology and Genetics, Karlsruhe Institute of Technology (KIT), Wolfgang-Gaede-Str. 1, 76131 Karlsruhe, Germany ...

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 # ...

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 ...