Lectures related to Recursive Least Squares

Intro to Quantum Sensing: Parameter Estimation and Fisher Information

Introduces Fisher Information for parameter estimation based on collected data.

Personalized Menu Optimization

Explores Bayesian methods in choice modeling for personalized menu optimization and individual choice prediction.

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Bias, Variance, Consistency, EMV

Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.

Estimation and Confidence Intervals

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Mixture models: taste heterogeneity MOOC: Selected Topics on Discrete Choice

Explores mixture models in discrete choice and random parameters estimation results.

Maximum Likelihood Inference

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

Debiased Whittle Likelihood: Time Series and Spatial Data

Explores the Debiased Whittle likelihood for time series and spatial data, focusing on fitting spectral density to the periodogram for better predictions and parameter estimation.

Behavior Modeling: Simple Example MOOC: Introduction to Discrete Choice Models

Introduces behavior modeling through a simple example, focusing on analyzing the market for electric cars.

Sampling Distributions: Estimation

Explores sampling distributions, estimation methods, and consistency in parameter estimation.

Introduction to Behavior Modeling: Simple Example

Introduces behavior modeling through a simple example, focusing on choice modeling components and the analysis of the electric car market.

Proof Technique for the Spiked Model

Covers a proof technique for the spiked model using matrix-based calculations and state evolution.

Latent Space Models for Multiplex Networks

Explores latent space models for multiplex networks, discussing network data representation, probabilistic models, and shared structure assumptions.

Maximum Likelihood Estimation: Properties and Consistency

Explores Maximum Likelihood Estimation properties, consistency, and applications in statistical inference.

Developing a Model

Covers the development of a model to identify stable causal relationships between variables.

Testing: t-tests

Covers t-tests, p-values calculation, and comparison of coefficients.

Generative Models: Logistic Regression & Gaussian Distribution

Explores generative models, logistic regression, and Gaussian distribution for approximating posterior probabilities and optimizing model performance.

Parameter Estimation

Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.

Parameter Estimation of SDEs with Linear Response Theory

Explores parameter estimation of SDEs using Linear Response Theory and covers challenges, examples, algorithms, and convergence.

Parameter Estimation for Deformable Objects

By David Millard explores parameter estimation for deformable objects in robotic manipulation tasks, focusing on the challenges and solutions in dealing with complex dynamics and using finite element techniques.