Results 71 to 80 of about 4,387 (252)

Time-sensitive RCTs in behavioral public policy: a pragmatic framework using sequence methods, personalization, and reinforcement learning

open access: yesFrontiers in Behavioral Economics
This article presents a pragmatic framework for time-sensitive analysis of behavioral RCTs using sequence methods and Markov modeling. The focus is not methodological novelty but translation: we map common policy questions to appropriate temporal tools ...
Giuseppe Alessandro Veltri   +1 more
doaj   +1 more source

Life Stage‐Specific Genetic Diversity and Landscape Characteristics Collectively Influence Genetic Recovery in Pometia pinnata Population 生活史阶段特异的遗传多样性与景观特征对番龙眼种群遗传恢复的协同影响

open access: yesIntegrative Conservation, EarlyView.
Pometia pinnata demonstrated life‐stage‐specific genetic responses to landscape features, with asymmetric gene flow patterns and population recovery following historical bottlenecks, revealing complex topographic and demographic influences on forest genetic structure.
Madhuparna Chatterjee   +5 more
wiley   +1 more source

Semispectral Measures and Feller markov Kernels

open access: yes, 2012
We give a characterization of commutative semispectral measures by means of Feller and Strong Feller Markov kernels. In particular: {itemize} we show that a semispectral measure $F$ is commutative if and only if there exist a self-adjoint operator $A$ and a Markov kernel $ _{(\cdot)}(\cdot): \times\mathcal{B}(\mathbb{R})\to[0,1]$, $ \subset (A ...
openaire   +2 more sources

Extending the hyper‐logistic model to the random setting: New theoretical results with real‐world applications

open access: yesMathematical Methods in the Applied Sciences, EarlyView.
We develop a full randomization of the classical hyper‐logistic growth model by obtaining closed‐form expressions for relevant quantities of interest, such as the first probability density function of its solution, the time until a given fixed population is reached, and the population at the inflection point.
Juan Carlos Cortés   +2 more
wiley   +1 more source

Ellipsoid‐Based Interval‐Type Uncertainty Model Updating Based on Riemannian Manifold and Gaussian Process Model

open access: yesInternational Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT Modern engineering systems require advanced uncertainty‐aware model updating methods that address parameter correlations beyond conventional interval analysis. This paper proposes a novel framework integrating Riemannian manifold theory with Gaussian Process Regression (GPR) for systems governed by Symmetric Positive‐Definite (SPD) matrix ...
Yanhe Tao   +3 more
wiley   +1 more source

Detecting extirpation: A localized approach to a global problem

open access: yesPLANTS, PEOPLE, PLANET, EarlyView.
The global biodiversity crisis stems from a cascading series of extirpations driving species toward extinction. Addressing this crisis requires methods for early detection of extinction at local scales, where communities can mobilize conservation efforts.
Andrew D. F. Simon   +4 more
wiley   +1 more source

Some Probabilistic Interpretations Related to the Next-Generation Matrix Theory: A Review with Examples

open access: yesMathematics
The fact that the famous basic reproduction number R0, i.e., the largest eigenvalue of the next generation matrix FV−1, sometimes has a probabilistic interpretation is not as well known as it deserves to be.
Florin Avram   +2 more
doaj   +1 more source

Clustering-Based Construction of Hidden Markov Models for Generative Kernels [PDF]

open access: yes, 2009
Generative kernels represent theoretically grounded tools able to increase the capabilities of generative classification through a discriminative setting. Fisher Kernel is the first and mostly-used representative, which lies on a widely investigated mathematical background.
Pekalska, Elzbieta   +5 more
openaire   +1 more source

Unsupervised Work Behavior Pattern Extraction Based on Hierarchical Probabilistic Model

open access: yesIEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
In this study, we address the challenge of analyzing worker behaviors in high‐mix, low‐volume production environments, where traditional supervised learning methods struggle owing to the lack of labeled data and task variability among workers. To overcome these issues, we propose a novel hierarchical approach for unsupervised behavior pattern ...
Issei Saito   +5 more
wiley   +1 more source

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