Projects for ECE-302: Probability Models and Stochastic Processes
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BayesianMLEstimation: Implementation of a Bayesian MMSE MLE estimators. Estimators are tested on various systems such as Rayleigh distributions and BPSK signals with interference. Results are comparatively analyzed.
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D&DSimulation: Simulations of the game Dungeons and Dragons are constructed and tested. Different random distributions are used to generate player characters and enemies with varying stats, as well as to calculate damage dealt in attacks. Results are plotted.
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Detection: Pattern recognition using machine learning. A MAP classifier is built, trained on the iris plant dataset. Simulations are run to estimate mean and covariance matrices, and a 4D pdf is computed.
Topics in probability, random variables and stochastic processes applied to the fields of electrical and computer engineering. Probability, events, random variables, expectation, moments, characteristic functions, conditional probability and expectation. Functions of random variables, random vectors, Gausian random vectors, Poisson points. Bounding and limit theorems. Relations among important distributions and probability models.Stochastic processes: stationarity, ergodicity, Brownian motion, Markov processes. Deterministic systems with stochastic inputs, correlation and power spectral density, ARMA models. Hilbert space and applications: orthogonality principle, discrete Wiener and Kalman filters, linear prediction, lattice filters.