- PhD in Statistics (University of São Paulo and UFSCar).
- Bachelor in Economics (University of São Paulo).
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Member of Stan development team.
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Fast Python data science practical course: pytutorial.marcoinacio.com.
Github profile: randommm.
Arxiv profile: dealmeidainacio_m_1.
LinkedIn: marcoinacio.
Apostila de Stan (Portuguese): marcoinacio.com/stan.
Curriculum Lattes: Portuguese English.
Published papers:
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Distance assessment and analysis of high-dimensional samples using variational autoencoders, Information Sciences (Marco Inácio, Rafael Izbicki, Bálint Gyires-Tóth).
DOI 10.1016/j.ins.2020.06.065. -
The NN-Stacking: Feature weighted linear stacking through neural networks, Neurocomputing (Victor Coscrato, Marco Henrique de Almeida Inácio and Rafael Izbicki).
DOI 10.1016/j.neucom.2020.02.073. -
Bayesian superposition of pure-birth destructive cure processes for tumor latency, Communications in Statistics - Simulation and Computation (Josemar Rodrigues, Marco Henrique de Almeida Inácio, Adriano K. Suzuki, Fernando Raimundo da Silva and Narayanaswamy Balakrishnan).
DOI 10.1080/03610918.2018.1538455. -
Comparing two populations using Bayesian Fourier series density estimation, Communications in Statistics - Simulation and Computation (Marco Henrique de Almeida Inácio, Rafael Izbicki and Luis Ernesto Salasar).
DOI 10.1080/03610918.2018.1484480.
Tutorials, software and stuff:
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Introductory Python tutorial, focused specially on statisticians and machine learning: pytutorial.marcoinacio.com.
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Para baixar a apostila de introdução ao Stan ou demais materiais em português, veja: marcoinacio.com/port.
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Python package which implements a density comparison method (for two samples) and a Fourier series nonparametric density estimation method: npcompare.
Interests:
- Professional interests: Statistics and machine learning, specially artificial neural networks, variational inference, variational autoencoders, Markov chain Monte Carlo and Bayesian inference. Programming (specially Python, C++, and R) and Probability.