Limit Theorems for Multiply Indexed Mixing Random Variables, with Application to Gibbs Random Fields
If $d$ is a fixed positive integer, let $\Lambda_N$ be a finite subset of $Z^d$, the lattice points of $\mathbb{R}^d$, with $\Lambda_N \uparrow Z^d$ and satisfying ...
Random fields provide a versatile mathematical framework to describe spatially dependent phenomena, ranging from physical systems and quantum chaos to cosmology and spatial statistics. Underpinning ...
Limit theorems are proved for two stochastic models of molecular evolution in finite populations of fixed size. An additive fitness model is shown to be asymptotically neutral in the sense that the ...
Stochastic processes form the backbone of modern probability theory, describing systems that evolve randomly over time or space. They are instrumental in areas ranging from statistical physics to ...
The Central Limit Theorem is a statistical concept applied to large data distributions. It says that as you randomly sample data from a distribution, the means and standard deviations of the samples ...
Animator Shuyi Chiou and the folks at CreatureCast give an adorable introduction to the central limit theorem – an important concept in probability theory that can reveal normal distributions (i.e.
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