Modeling Neurobiological Signals: Markov Chains

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Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Covers Markov Chain Monte Carlo examples and error estimation methods.

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.

Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.

Explores modeling neurobiological signals, focusing on spikes, firing rate, multiple state neurons, and parameter estimation.

Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.

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

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.