Likelihood of a spike train

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

Likelihood Ratio Test: Hypothesis Testing

Covers the Likelihood Ratio Test and hypothesis testing methods using 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 Ratio Test: Detection & Estimation

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

Maximum Likelihood Estimation: Econometrics

Introduces Maximum Likelihood Estimation in econometrics, covering principles, properties, applications, and specification tests.

Latent Variable Models

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

Discrete Choice Analysis

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Interspike Intervals & Renewal Processes

Explores interspike intervals, renewal processes, and escape noise experiments in neuronal dynamics.

Spin Glasses and Bayesian Estimation

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

The Likelihood of Data under a Model

Explores the likelihood of data under a model and the concept of maximum likelihood.