Results 31 to 40 of about 96,717 (274)

Factors of alternating convolution of the Gessel numbers [PDF]

open access: yesNotes on Number Theory and Discrete Mathematics
The Gessel number P(n,r) is the number of lattice paths in the plane with (1,0) and (0,1) steps from (0,0) to (n+r, n+r-1) that never touch any of the points from the set {(x,x)∈ℤ²:x≥r}. We show that there is a close relationship between Gessel numbers P(
Jovan Mikić
doaj   +1 more source

A rapidly convergent method for solving third-order polynomials

open access: yesAIP Advances, 2022
We present a rapidly convergent method for solving cubic polynomial equations with real coefficients. The method is based on a power series expansion of a simplified form of Cardano’s formula using Newton’s generalized binomial theorem.
Ramón A. Fernández Molina   +3 more
doaj   +1 more source

Sharp Estimates for Proximity of Geometric and Related Sums Distributions to Limit Laws

open access: yesMathematics, 2022
The convergence rate in the famous Rényi theorem is studied by means of the Stein method refinement. Namely, it is demonstrated that the new estimate of the convergence rate of the normalized geometric sums to exponential law involving the ideal ...
Alexander Bulinski, Nikolay Slepov
doaj   +1 more source

A q-rious positivity

open access: yes, 2010
The $q$-binomial coefficients $\qbinom{n}{m}=\prod_{i=1}^m(1-q^{n-m+i})/(1-q^i)$, for integers $0\le m\le n$, are known to be polynomials with non-negative integer coefficients.
Warnaar, S. Ole, Zudilin, Wadim
core   +1 more source

Restricted Tweedie stochastic block models

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero‐inflated continuous edge weights.
Jie Jian, Mu Zhu, Peijun Sang
wiley   +1 more source

Lucas' theorem: its generalizations, extensions and applications (1878--2014) [PDF]

open access: yes, 2014
In 1878 \'E. Lucas proved a remarkable result which provides a simple way to compute the binomial coefficient ${n\choose m}$ modulo a prime $p$ in terms of the binomial coefficients of the base-$p$ digits of $n$ and $m$: {\it If $p$ is a prime, $n=n_0 ...
Meštrović, Romeo
core  

Markov branching processes with disasters: extinction, survival and duality to p-jump processes

open access: yes, 2019
A $p$-jump process is a piecewise deterministic Markov process with jumps by a factor of $p$. We prove a limit theorem for such processes on the unit interval.
Hermann, F., Pfaffelhuber, P.
core   +1 more source

Modeling the Probability of Tsunami Fire Ignition Based on Data From the 2011 Tohoku and 2024 Noto Peninsula Earthquakes, With Recommendations to Reduce Emerging Fire Risk in Tsunami Vertical Evacuation Structures

open access: yesEarthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Recent tsunamigenic earthquakes in Japan have highlighted the emerging fire hazard triggered by tsunami inundation and its impact on tsunami vertical evacuation (TVE) structures. This new type of fire following earthquake, referred to as “tsunami fires,” may be a potential universal hazard that tsunami‐prone countries face; however, it has not
Tomoaki Nishino
wiley   +1 more source

Stochastic Ordering of Exponential Family Distributions and Their Mixtures

open access: yes, 2009
We investigate stochastic comparisons between exponential family distributions and their mixtures with respect to the usual stochastic order, the hazard rate order, the reversed hazard rate order, and the likelihood ratio order.
Hardy   +6 more
core   +1 more source

Scaling‐Aware Rating of Poisson‐Limited Demand Forecasts

open access: yesJournal of Forecasting, EarlyView.
ABSTRACT Forecast quality should be assessed in the context of what is possible in theory and what is reasonable to expect in practice. Often, one can identify an approximate upper bound to a probabilistic forecast's sharpness, which sets a lower, not necessarily achievable, limit to error metrics.
Malte C. Tichy   +4 more
wiley   +1 more source

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