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Under a risk-neutral measure, the discounted stock must be a martingale. If $q$ is the probability of the factor $1+b$, this condition issoThe hypothesis $a<r<b$ ensures $0<q<1$, making this equivalent to the original probability measure. Since the $N$ returns are independent under $Q$, the number of up moves has a binomial distribution, and hence

For fixed disjoint $A,B$, the random variable $e(A,B)$ has the binomial distribution $\mathrm{Bin}(|A||B|,p)$. A two-sided Chernoff bound givesWhen $|A|,|B|\ge n/\log n$, this is at mostThere are at most $3^n$ ordered disjoint pairs $(A,B)$, since each vertex can lie in $A$, in $B$, or in neither. The union bound therefore makes the probability of any failure at mostwhich proves the simultaneous estimate.

LetHere $S_n$ has the binomial distribution with parameters $(n,\lambda /n)$. Part a, now with independent coordinates, givesBy Pinsker's inequality, the probability mass functions therefore converge in total variation, and in particular $S_n$ converges in distribution to $Z\sim \mathrm{Poisson}(\lambda )$.

The joint probability of the observed row depends only on $S_n$:Taking logarithms in any fixed base and choosing $c_n=\log n$ giveswhereThe convergence lemma supplied in the question now yieldsThis sparse triangular array therefore has a random limiting normalized self-information rather than the constant limit in the usual asymptotic equipartition property.

Explore the connected component of a graph containing a fixed vertex $v$ by Breadth-first search. If the exploration discovers at least $m$ vertices, then before its $m$th discovery at least $m-1$ of at most $mn$ tested potential edges must be present. The tests are independent Bernoulli trials with parameter $p=(1-\epsilon )/n$, soThe binomial distribution on the right has mean $(1-\epsilon )m$. For $m\ge 2/\epsilon$, the exponential Markov bound givesfor an absolute constant $c>0$. The union bound over the $n$ choices of $v$ now givesTaking $m=C_{\epsilon }\log n$ with $C_{\epsilon }>2/(c{\epsilon }^2)$ makes this probability tend to zero. This proves the subcritical component bound for a binomial random graph.

Under the boundary null $p={\pi }_0=0.20$, rejection requires both $X_1>r_1$ and $X_1+X_2\ge r$. Independence of the two binomial distributions givesThe rejection event is increasing in $p$, so this boundary value is the supremum over the composite null $p\le {\pi }_0$.

For $n$ players with independent valuations having a continuous distribution function $F$, a type $v$ occupies rank $k$ when exactly $k-1$ rivals have higher values. Multiplying this binomial distribution by the rank prize $w_k$ and summing gives $a(v)$. Ties occur only on zero-probability events when the valuation law is atomless.

Place $n$ labelled balls independently into $m$ labelled bins, each with probability $1/m$. The vector of bin counts has a multinomial distribution, and each count separately has the binomial distribution $\mathrm{Bin}(n,1/m)$. Distinct bin counts have covariance $-n/m^2$, so they are dependent for $n>0$ and $m>1$. Counts of bins of a specified size are sums of indicator variables; their expectations follow from the linearity of expectation, without any independence between bins.

A uniform prior on a finite-volume parameter region assigns constant probability density there and zero outside. On $[0,1]$ it is $\mathrm{Beta}(1,1)$, giving a Beta distribution posterior after binomial distribution data. Uniformity depends on the coordinate: a nonlinear reparametrization introduces a Jacobian and generally does not preserve a uniform density.

Start with the empty graph and reveal the edges of the complete graph in a uniformly random order. After $m$ steps its edge set is a uniform $m$-element subset. Independent uniform edge labels couple this process with all binomial random graphs: $G(n,p)=G_{n,M(p)}$, where $M(p)$ has a binomial distribution with parameters $\left(\genfrac{}{}{0ex}{}{n}{2}\right),p$. For a monotone graph property, concentration of $M(p)$ transfers a threshold estimate between edge probability and edge count.