Probability distributions are fundamental tools in statistics and data science, allowing us to model the likelihood of different outcomes in a random event. While we often work with complete and ...

Sampling from probability distributions with known density functions (up to normalization) is a fundamental challenge across various scientific domains. From Bayesian uncertainty quantification to ...

In this paper, we consider the function f p ( t )= 2p X 2 ( 2p t+p;p ) , where χ²(x; n) defined by X 2 ( x;p )= 2 −p/2 Γ( p/2 ) e −x/2 x p/2−1 , is the density function of a χ²-distribution with n ...

Abstract: While probability distribution functions are crucial for simulating random processes, research on these functions and their features is required. However, studies have demonstrated that in ...

1 Department of Plant Pathology, The Ohio State University, Wooster, OH, United States 2 Center for Integrated Fungal Research, Department of Entomology and Plant Pathology, North Carolina State ...

1 Department of Business Administration and LaboMaths, University Julius Nyéréré of Kankan, Kankan, Guinea. 2 Faculty of Sciences, University Julius Nyéréré of Kankan, Kankan, Guinea. Several ...

dxxx(x,) returns the density or the value on the y-axis of a probability distribution for a discrete value of x pxxx(q,) returns the cumulative density function (CDF) or the area under the curve to ...

Probability distribution is an essential concept in statistics, helping us understand the likelihood of different outcomes in a random experiment. Whether you’re a student, researcher, or professional ...

Quantum annealing (QA) can be competitive to classical algorithms in optimizing continuous-variable functions when running on appropriate hardware, show researchers from Tokyo Tech. By comparing the ...