Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Poisson Process Approach
Graph Chatbot
Related lectures (31)
Previous
Page 3 of 4
Next
Exponential Family: Properties and Estimation
Explores exponential families, Bernoulli distributions, parameter estimation, and maximum entropy distributions in statistical modeling.
Binary Covariate Impact: 2x2 Contingency Tables
Explores the impact of a binary covariate on binary responses using 2x2 tables.
Statistical Theory: Inference and Optimality
Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.
Likelihood Inference
Covers iterative weighted least squares, Poisson regression, mixed models, and likelihood ratio statistic.
Elements of Statistics: Memorylessness, Stationary Processes, Estimation using MLE
Explores memorylessness in distributions, stationary processes, and estimation using MLE.
The Likelihood of Data under a Model
Explores the likelihood of data under a model and the concept of maximum likelihood.
Generalised Linear Models: Regression with Exponential Family Responses
Covers regression with exponential family responses using Generalised Linear Models.
Escape noise
Explores escape noise in computational neuroscience, covering stochastic intensity, interspike intervals, likelihood functions, noise model comparison, and rate versus temporal codes.
Latent Variable Models
Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.
Modeling Neurobiological Signals: Spikes & Firing Rate
Explores modeling neurobiological signals, focusing on spikes, firing rate, multiple state neurons, and parameter estimation.