Lectures related to Poisson Processes Theorems

Simulation & Optimization: Poisson Process & Random Numbers

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.

Escape noise MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores escape noise in computational neuroscience, covering stochastic intensity, interspike intervals, likelihood functions, noise model comparison, and rate versus temporal codes.

Likelihood Ratio Test: Detection & Estimation

Covers the likelihood ratio test for detection and estimation in statistical analysis.

Maximum Likelihood Inference

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

Binary Covariate Impact: 2x2 Contingency Tables

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Point Processes: Spatial Analysis

Explores point processes in spatial analysis, focusing on spatial object dissemination and pattern detection.

Likelihood Ratio Test: Hypothesis Testing

Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

Linear Regression: Statistical Inference Perspective

Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.

Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Likelihood of a spike train MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Discusses the likelihood of spike trains based on generative models and log-likelihood calculations from observed data.

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Likelihood Ratio Test: Hypothesis Testing

Explores hypothesis testing, emphasizing the likelihood ratio test and its applications in statistical analysis.

Poisson Process Approach Log in to Mediaspace to watch this video

Explores the Poisson process approach in extreme value analysis, emphasizing component-wise transformations and likelihood functions for extreme events.

Extreme Value Theory: Statistical Applications Log in to Mediaspace to watch this video

Explores statistical applications of threshold exceedances and GPD modeling in extreme value theory.

Estimating R: Theory of Poisson Processes Log in to Mediaspace to watch this video

Covers the theory of Poisson processes and the estimation of the intensity parameter R.

Extreme Value Theory: Point Processes Log in to Mediaspace to watch this video

Covers the application of extreme value theory to point processes and the estimation of extreme events from equally-spaced time series.

Poisson Process: Density Theory and Applications Log in to Mediaspace to watch this video

Explores Poisson processes, joint density, independence of events, and likelihood estimation.

Mapping and Colouring: Poisson Processes Log in to Mediaspace to watch this video

Covers the theorems of superposition and colouring for Poisson processes.

Jacamar Data Analysis Log in to Mediaspace to watch this video

Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.

Estimating R: Marking and Convergence Log in to Mediaspace to watch this video

Covers the estimation of R in Poisson processes, focusing on marking points and convergence.