Open-source tools have made MMM more accessible, but reliable results still depend on clean data, thoughtful modeling, and ...

Arthur Pinkasovitch, CFA, has worked 5+ years as a financial analyst. He is an associate director at ATB Financial. David Kindness is a Certified Public Accountant (CPA) and an expert in the fields of ...

Qubits, the heart of quantum computers, can change performance in fractions of a second — but until now, scientists couldn’t see it happening. Researchers at NBI have built a real-time monitoring ...

Traditional statistical and machine learning methods mostly focus on correlations, but causal models allow researchers to infer mechanisms and predict the effects of interventions. Nevertheless, the ...

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

PyBADS is a Python implementation of the Bayesian Adaptive Direct Search (BADS) algorithm for solving difficult and mildly expensive optimization problems, originally implemented in MATLAB. BADS has ...

Bambi is a high-level Bayesian model-building interface written in Python. It's built on top of the PyMC probabilistic programming framework, and is designed to make it extremely easy to fit ...

Drift diffusion models (DDMs) are pivotal in understanding evidence accumulation processes during decision-making across psychology, behavioral economics, neuroscience, and psychiatry. Hierarchical ...

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...