About the Probability-Field-Intersections of Weichselberger and a Simple Conclusion from Least Favorable Pairs

Author(s):  
Martin Gümbel
Keyword(s):  
1981 ◽  
Vol 8 (2) ◽  
pp. 103-105
Author(s):  
R. Walton Mozingo

Abstract Knowledge of the fruiting patterns of peanuts (Arachis hypogaea L.) is useful in determining the most feasible band widths for pesticides and other amendments, designing new peanut equipment and adapting new production practices for specific cultivars. The fruiting patterns and the effect of field traffic on these patterns were determined on five large-seeded Virginia type peanut cultivars with variant growth habits. Cultivars differed in fruiting patterns with the bunch growth type having a significantly higher percentage of their pods near the taproot than the runner types. The bunch types, NC 17 and NC-FLA 14 had 73.5 and 66.5% of their total fruit within a 13-cm wide section centered over the taproot whereas, the runners, VA 72R and Florigiant, and the intermediate type, NC 5, had 42.8, 45.5 and 45.3%, respectively, in a similar position. At the 0.05 level of probability, field traffic had no significant effect on the fruit distribution of the bunch cultivars, but NC 5, VA 72R and Florigiant (at 0.10 level) produced significantly less fruit on the side of the row subjected to field traffic during the fruiting season.


2021 ◽  
Author(s):  
Moshe Szweizer ◽  
Rivka Schlagbaum

In the article, it is shown that the concept of mass can be arrived at through a consideration of two probability fields interacting with each other. The interaction is subject to discontinuities. These, in turn, when being traversed, pose a resistance, which is perceived as mass. Thus, mass is a manifestation of discontinuity in the probability field. The approach allows for the retrieval of masses of elementary particles, providing high agreement with the experimental data. It also explains the longevity of the proton and explains why other heavy particles are short-lived. Moreover, the model presented in the paper sheds light on the nature of weak interactions.


Author(s):  
J. H. Pacheco-Sánchez ◽  
R. D. Vera-Torres ◽  
R. Alejo

Bayesian learning is applied on two class systems. Partitioning a big sample made up of many elements of two classes of indistinguishable objects, we indistinctly pursue from 2 to 5 training sets called hypotheses in the probability field, with a plausible rate of object from each hypothesis. Objects are taken one by one from the sample. The basic aim faced is to predict one type of objects in the following occasion in which an agent takes one of them from the original sample to test it. We obtain the graph of a posteriori probability for each hypothesis of one of the objects. A prediction that the following object is specifically one of them is acquired in one probability curve by means of training previously accomplished. This methodology is applied on manufacture of glass bottles of two classes: good or crash. The main interest is to predict which machine produced one detected crash bottle because bottles turn to be indistinguishable when they are reviewed. This is solved by fixing a priori probabilities and taking into account all possible probability distribution combinations in the classes.


2009 ◽  
Vol 16 (01) ◽  
pp. 85-100 ◽  
Author(s):  
Elio Conte ◽  
Andrei Yuri Khrennikov ◽  
Orlando Todarello ◽  
Antonio Federici ◽  
Leonardo Mendolicchio ◽  
...  

Processes undergoing quantum mechanics exhibit quantum interference effects. In this case, quantum probabilities result to be different from classical ones because they contain an additional so-called quantum interference term. We use ambiguous figures to analyse if during perception-cognition by human subjects we can observe violation of the classical probability field and the presence of quantum interference. The experiments, conducted on a group of 256 subjects, evidence that we indeed have such a quantum effect. Therefore, mental states, during perception and cognition of ambiguous figures, appear to follow quantum mechanics.


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