The Bayes’ theorem is a mathematical formula to gauge and describe the probability of an event by employing prior knowledge evidence relevant event. objective present study was understand Bayesian its application in judicial trials deploying doctrinal research methodology. After consulting authoritative writings prominent researchers decisions, found that Probability legal context used odds ver...

Quantitative studies of irreversibility in statistical mechanics often involve the consideration a reverse process, whose definition has been object many discussions, particular for quantum mechanical systems. Here we show that channel very naturally arises from Bayesian retrodiction, both classical and theories. Previous paradigmatic results, such as Jarzynski's equality, Crooks' fluctuation t...

Bayesian reasoning, defined here as the updating of a posterior probability following new information, has historically been problematic for humans. Classic psychology experiments have tested human Bayesian reasoning through the use of word problems and have evaluated each participant's performance against the normatively correct answer provided by Bayes' theorem. The standard finding is of gen...

Classification is an important data mining technique that is used by many applications. Several types of classifiers have been described in the research literature. Example classifiers are decision tree classifiers, rule-based classifiers, and neural networks classifiers. Another popular classification technique is naïve Bayesian classification. Naïve Bayesian classification is a probabilistic ...

in this paper, after reviewing some results in minimal space, some new results in this setting are given. we prove a generalized form of the fan-kkm typetheorem in minimal vector spaces. as some applications, the open type of matching theorem and generalized form of the classical kkm theorem in minimal vector spaces are given.

K-means clustering algorithm is a method of cluster analysis which aims to partition n observations into clusters in which each observation belongs to the cluster with the nearest mean. It is one of the simplest unconfirmed learning algorithms that solve the well known clustering problem. It is similar to the hope maximization algorithm for mixtures of Gaussians in that they both attempt to fin...

A mechanism coalitionally implements a social choice set if any outcome of the social choice set can be achieved as a coalitional Bayesian Nash equilibrium of a mechanism and vice versa. We say that a social choice set is coalitionally implementable if there is a mechanism which coalitionally implements it. Our main theorem proves that a social choice set is coalitionally implementable if and o...

Data uncertainty can be caused by numerous factors such as measurement precision limitations, network latency, data staleness and sampling errors. When mining knowledge from emerging applications such as sensor networks or location based services, data uncertainty should be handled cautiously to avoid erroneous results. In this paper, we apply probabilistic and statistical theory on uncertain d...

This paper proposes a method for Document Retrieval Systems. The document retrieval system finds information to given criteria by matching text record (documents) against user queries. The results generated from information retrieval system must have user preferences. Each user has its own perspectives and cultural context of each word or when the user is searching for highly specific, focussed...

Censored (or truncated) observations are quite prevalent in practice. How does the existence of imperfect observations affect optimal decisions? We consider this problem in the setting of a general finite-horizon Bayesian sequential decision process. We first prove a general derivative result that resembles the classic envelope theorem. We then show that for a class of Bayesian sequential decis...