Utilization distribution - Wikipedia
A utilization distribution is a probability distribution giving the probability density that an animal is found at a given point in space. It is estimated
protegeproject/protege-distribution
Build files for the protege distribution that includes 3rd party plugins (⭐ 151)
The Distributed Bloom Filter
The Distributed Bloom Filter is a space-efficient, probabilistic data structure designed to perform more efficient set reconciliations in distributed systems. It guarantees eventual consistency of states between nodes in a system, while still keeping...
Distribution fitting 12. Sampling distribution of compounds abundance from plant species m…
A series of ten plant species belonging to Magnoliopsida - Dicotyledons class were analyzed in terms of chemical compounds distribution of abundance, starting from the assumption that these distributions should give a picture of similarities and diff...
probability - Cumulative Distribution function of a Poisson ...
Sep 18, 2019 · This is because the CDF of Poisson distribution is related to that of a Gamma distribution. Hence the incomplete gamma function.
Poisson distribution - Wikipedia
an excellent approximation to the Poisson distribution. If λ is greater than about 10, then the normal distribution is a good approximation if an appropriate
probability - Distribution of Event Times in a Poisson Process ...
Sep 5, 2024 · Normally, everyone talks about the distribution of interarrival times in a Poisson Process are Exponential ... but what about the distribution of the actual event times?
Time Series Prediction under Distribution Shift using Differentiable Forgetting
Time series prediction is often complicated by distribution shift which demands adaptive models to accommodate time-varying distributions. We frame time series prediction under distribution shift as a weighted empirical risk minimisation problem. The...
Laplace distribution - Wikipedia
theory and statistics, the Laplace distribution is a continuous probability distribution named after Pierre-Simon Laplace. It is also sometimes called
Theoretical Study of Optimizing Rugged Landscapes with the cGA
Estimation of distribution algorithms (EDAs) provide a distribution - based approach for optimization which adapts its probability distribution during the run of the algorithm. We contribute to the theoretical understanding of EDAs and point out that...
PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm
In this paper, we propose the first fully push-forward-based distributional reinforcement learning algorithm, named PACER, which consists of a distributional critic, a stochastic actor and a sample-based encourager. Specifically, the push-forward ope...
JuliaStats/Distributions.jl
A Julia package for probability distributions and associated functions. (⭐ 1186)
The distribution of landed property
The distribution of property is established through various mechanisms. In this paper we study the acreage distribution of land plots owned by natural persons in the Zlín Region of the Czech Republic. We show that the data are explained in terms o...
Are distribution tools worth it?
I typically do stockfleth and have for years since I worked in a shop. Do you guys with distribution tools feel that they have significantly decreased channeling or other distribution related flaws? ...
Stationary distribution - Wikipedia
Stationary distribution may refer to: Discrete-time Markov chain § Stationary distributions and continuous-time Markov chain § Stationary distribution, a special
JustEnoughLinuxOS/distribution
Home of the JELOS Linux distribution. (⭐ 946)
A polynomial distribution applied to income and wealth distribution
Income and wealth distribution affect stability of a society to a large extent and high inequality affects it negatively. Moreover, in the case of developed countries, recently has been proven that inequality is closely related to all negative phenom...
nodesource/distributions
NodeSource Node.js Binary Distributions (⭐ 13821)
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Categorical distributions are ubiquitous in machine learning, e.g., in classification, language models, and recommendation systems. However, when the number of possible outcomes is very large, using categorical distributions becomes computationally e...
Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluation…
This paper reexamines the research on out-of-distribution (OOD) robustness in the field of NLP. We find that the distribution shift settings in previous studies commonly lack adequate challenges, hindering the accurate evaluation of OOD robustness. T...