In [14] we formalized probability and probability distribution on a finite sample space. In this article first we propose a formalization of the class of finite sample spaces whose element’s probability distributions are equivalent with each other. Next, we formalize the probability measure of the class of sample spaces we have formalized above. Finally, we formalize the sampling and posterior probability
This paper considers a new class \Gamma specified under uncertainty on the relative weights of some ...
This paper contains a complete class theorem (Theorem 3.2) which applies to most statistical estimat...
We present two techniques for constructing sample spaces that approximate probability distributions....
In this article, we describe a Bayesian approach for the estimation of probability distribution of a...
Assuming that the sample space is discrete and sampling distributions assign positive probability to...
Linear spaces consisting of $\sigma$-finite probability measures and infinite measures (improper pri...
Linear spaces consisting of $\sigma$-finite probability measures and infinite measures (improper pri...
Linear spaces consisting of -finite probability measures and infinite measures (improper priors an...
Linear spaces consisting of o-finite probability measures and infinite measures (improper priors and...
A pivotal problem in Bayesian nonparametrics is the construction of prior distributions on the space...
We introduce a set of transformations on the set of all probability distributions over a finite stat...
In this section we recall the basic vocabulary and results of probability theory. A probability spac...
We have been working on the formalization of the probability and the randomness. In [15] and [16], w...
An abstract definition of probability can be given by considering a set S, called the sample space, ...
This paper contains a complete class theorem (Theorem 3.2) which applies to most statistical estimat...
This paper considers a new class \Gamma specified under uncertainty on the relative weights of some ...
This paper contains a complete class theorem (Theorem 3.2) which applies to most statistical estimat...
We present two techniques for constructing sample spaces that approximate probability distributions....
In this article, we describe a Bayesian approach for the estimation of probability distribution of a...
Assuming that the sample space is discrete and sampling distributions assign positive probability to...
Linear spaces consisting of $\sigma$-finite probability measures and infinite measures (improper pri...
Linear spaces consisting of $\sigma$-finite probability measures and infinite measures (improper pri...
Linear spaces consisting of -finite probability measures and infinite measures (improper priors an...
Linear spaces consisting of o-finite probability measures and infinite measures (improper priors and...
A pivotal problem in Bayesian nonparametrics is the construction of prior distributions on the space...
We introduce a set of transformations on the set of all probability distributions over a finite stat...
In this section we recall the basic vocabulary and results of probability theory. A probability spac...
We have been working on the formalization of the probability and the randomness. In [15] and [16], w...
An abstract definition of probability can be given by considering a set S, called the sample space, ...
This paper contains a complete class theorem (Theorem 3.2) which applies to most statistical estimat...
This paper considers a new class \Gamma specified under uncertainty on the relative weights of some ...
This paper contains a complete class theorem (Theorem 3.2) which applies to most statistical estimat...
We present two techniques for constructing sample spaces that approximate probability distributions....