Panel data: dynamic model with panel effects

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Modeling Neurobiological Signals: Markov Chains

Explores modeling neurobiological signals with Markov Chains, focusing on parameter estimation and data classification.

Panel data: dynamic model

Explores the dynamic model for panel data analysis and the Markov assumption.

Monte Carlo: Markov Chains

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

Learning Chemical Reaction Networks

Explores sparse learning of chemical reaction networks from trajectory data using data-based methods and learning approaches.

Hidden Markov Models: Primer

Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.

Hidden Markov Models (HMM): Theory

Covers Hidden Markov Models (HMM) for modeling time series data and decoding using the Viterbi Algorithm.

MCMC Examples and Error Estimation

Covers Markov Chain Monte Carlo examples and error estimation methods.

Stochastic Processes: Markov Chains

Covers stochastic processes, focusing on Markov chains and their applications in real-world scenarios.

EM Algorithm: Lite-bulbs Lifetime Estimation

Explains the EM algorithm for estimating lite-bulbs lifetime and discusses its application to heavy-bulbs.

Discrete Panel Data

Explores discrete panel data, covering static and dynamic models with panel effects and their practical implications.