Recursive Least Squares

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

Modeling Neurobiological Signals: Markov Chains

Explores modeling neurobiological signals with Markov Chains, focusing on parameter estimation and data classification.

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.

Extreme Value Analysis: Threshold Selection

Discusses threshold selection in extreme value analysis and its impact on model performance.

Behavior Modeling: Simple Example

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.