Quantifying Randomness and Information in Biological Data

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Page 3 of 4

Explores mutual information in biological data, emphasizing its role in quantifying statistical dependence and analyzing protein sequences.

Introduces random variables, probability distributions, and expected values through practical examples.

Delves into quantifying statistical dependence through covariance, correlation, and mutual information.

Covers source coding theorems, entropy, and various source models in information theory.

Compares random and symmetrical graph coloring in terms of cluster colorability and equilibrium.

Explores maximum entropy modeling applications in neuroscience and protein sequence data.

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Explores entropy in information theory and its role in data processing and probability distributions.

Discusses estimation and propagation of uncertainty in random variables and the importance of managing uncertainty in statistical analysis.

Explores random walks, Moran model, bacterial chemotaxis, entropy, information theory, and coevolving sites in proteins.