Approaching probabilistic and deterministic nomic truths in an inductive probabilistic way

Theories of truth approximation in terms of truthlikeness (or verisimilitude) almost always deal with (non-probabilistically) approaching deterministic truths, either actual or nomic. This paper deals first with approaching a probabilistic nomic truth, viz. a true probability distribution. It assumes a multinomial probabilistic context, hence with a lawlike true, but usually unknown, probability distribution. We will first show that this true multinomial distribution can be approached by Carnapian inductive probabilities. Next we will deal with the corresponding deterministic nomic truth, that is, the set of conceptually possible outcomes with a positive true probability. We will introduce Hintikkian inductive probabilities, based on a prior distribution over the relevant deterministic nomic theories and on conditional Carnapian inductive probabilities, and first show that they enable again probabilistic approximation of the true distribution. Finally, we will show, in terms of a kind of success theorem, based on Niiniluoto’s estimated distance from the truth, in what sense Hintikkian inductive probabilities enable the probabilistic approximation of the relevant deterministic nomic truth. In sum, the (realist) truth approximation perspective on Carnapian and Hintikkian inductive probabilities leads to the unification of the inductive probability field and the field of truth approximation.

Probability Field in Chaotic Intra-Entropic Interactions

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.

Bayesian Learning on Discrete Systems of Two Classes

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.

Mining Frequent Items in Uncertain Dataset

Because of uncertainty data, traditional algorithm of mining frequent items in certain dataset is difficult to apply to uncertain dataset. Considering characteristics of uncertain data, an improved vertical mining algorithm to find frequent items in uncertain dataset was proposed with the algorithm thought of classic vertical algorithm-Eclat in certain dataset. The improved algorithm merged TID field and corresponding probability field into probability vector. During the expansion of itemset and probability vector, itemset tree was established, and the support of candidate itemsets was calculated by means of vector operations. The improved algorithm is proved to be feasible and efficient according to experimental comparison and analysis.

Mental States Follow Quantum Mechanics During Perception and Cognition of Ambiguous Figures

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.