Invariance of nonatomic measures on effect algebras

2018 ◽  
Vol 68 (2) ◽  
pp. 311-318
Author(s):  
Akhilesh Kumar Singh
Keyword(s):  
Probability Measure ◽  
Effect Algebra ◽  
Probability Measures ◽  
Effect Algebras ◽  
Darboux Property

Abstract The present paper deals with invariance of nonatomic measures defined on effect algebras. Firstly, it is proved that if μ is a nonatomic and continuous probability measure defined on a σ-complete effect algebra L, then it satisfies para-Darboux property. Then, the invariance between continuous probability measures m and μ defined on a σ-complete effect algebra L is established when μ is nonatomic satisfying para-Darboux property on L.

scholarly journals A note on the idempotent measures on countable semigroups

1970 ◽  
Vol 11 (4) ◽  
pp. 417-420
Author(s):  
Tze-Chien Sun ◽  
N. A. Tserpes
Keyword(s):  
Probability Measure ◽  
Continuous Mapping ◽  
Compact Semigroup ◽  
Probability Measures ◽  
Left Zero ◽  
Product Topology ◽  
Locally Compact ◽  
Locally Compact Semigroup ◽  
Image Position ◽  
Completely Simple

In [6] we announced the following Conjecture: Let S be a locally compact semigroup and let μ be an idempotent regular probability measure on S with support F. Then(a) F is a closed completely simple subsemigroup.(b) F is isomorphic both algebraically and topologically to a paragroup ([2], p.46) X × G × Y where X and Y are locally compact left-zero and right-zero semi-groups respectively and G is a compact group. In X × G × Y the topology is the product topology and the multiplication of any two elements is defined by , x where [y, x′] is continuous mapping from Y × X → G.(c) The induced μ on X × G × Y can be decomposed as a product measure μX × μG× μY where μX and μY are two regular probability measures on X and Y respectively and μG is the normed Haar measure on G.

Central Limit Theorems for Interchangeable Processes

1958 ◽  
Vol 10 ◽  
pp. 222-229 ◽  
Author(s):  
J. R. Blum ◽  
H. Chernoff ◽  
M. Rosenblatt ◽  
H. Teicher
Keyword(s):  
Stochastic Process ◽  
Probability Measure ◽  
Central Limit ◽  
Joint Distribution ◽  
Limit Theorems ◽  
Random Variables ◽  
Central Limit Theorems ◽  
Probability Measures ◽  
Image Position ◽  
Positive Integers

Let {Xn} (n = 1, 2 , …) be a stochastic process. The random variables comprising it or the process itself will be said to be interchangeable if, for any choice of distinct positive integers i 1, i 2, H 3 … , ik, the joint distribution of depends merely on k and is independent of the integers i 1, i 2, … , i k. It was shown by De Finetti (3) that the probability measure for any interchangeable process is a mixture of probability measures of processes each consisting of independent and identically distributed random variables.

Perfect residuated lattice ordered monoids

2010 ◽  
Vol 60 (6) ◽  
Author(s):  
Jiří Rachůnek ◽  
Dana Šalounová
Keyword(s):  
Probability Measure ◽  
Residuated Lattice ◽  
Residuated Lattices ◽  
Heyting Algebras ◽  
Effect Algebras ◽  
Quantum Logics ◽  
Mv Algebras ◽  
Intuitionistic Logics

AbstractBounded Rℓ-monoids form a large subclass of the class of residuated lattices which contains certain of algebras of fuzzy and intuitionistic logics, such as GMV-algebras (= pseudo-MV-algebras), pseudo-BL-algebras and Heyting algebras. Moreover, GMV-algebras and pseudo-BL-algebras can be recognized as special kinds of pseudo-MV-effect algebras and pseudo-weak MV-effect algebras, i.e., as algebras of some quantum logics. In the paper, bipartite, local and perfect Rℓ-monoids are investigated and it is shown that every good perfect Rℓ-monoid has a state (= an analogue of probability measure).

scholarly journals Robust decision making and convex risk measures computation

2015 ◽  
Author(s):  
Γεώργιος Παπαγιάννης
Keyword(s):  
Decision Making ◽  
Probability Measure ◽  
Calculus Of Variations ◽  
Model Uncertainty ◽  
Risk Measures ◽  
Risk Measure ◽  
Probability Measures ◽  
Discrete Measure ◽  
Convex Risk Measures ◽  
Variance Risk

The main aim of the present thesis is to investigate the effect of diverging priors concerning model uncertainty on decision making. One of the main issues in the thesis is to assess the effect of different notions of distance in the space of probability measures and their use as loss functionals in the process of identifying the best suited model among a set of plausible priors. Another issue, is that of addressing the problem of ``inhomogeneous" sets of priors, i.e. sets of priors that highly divergent opinions may occur, and the need to robustly treat that case. As high degrees of inhomogeneity may lead to distrust of the decision maker to the priors it may be desirable to adopt a particular prior corresponding to the set which somehow minimizes the ``variability" among the models on the set. This leads to the notion of Frechet risk measure. Finally, an important problem is the actual calculation of robust risk measures. An account of their variational definition, the problem of calculation leads to the numerical treatment of problems of the calculus of variations for which reliable and effective algorithms are proposed. The contributions of the thesis are presented in the following three chapters. In Chapter 2, a statistical learning scheme is introduced for constructing the best model compatible with a set of priors provided by different information sources of varying reliability. As various priors may model well different aspects of the phenomenon the proposed scheme is a variational scheme based on the minimization of a weighted loss function in the space of probability measures which in certain cases is shown to be equivalent to weighted quantile averaging schemes. Therefore in contrast to approaches such as minimax decision theory in which a particular element of the prior set is chosen we construct for each prior set a probability measure which is not necessarily an element of it, a fact that as shown may lead to better description of the phenomenon in question. While treating this problem we also address the issue of the effect of the choice of distance functional in the space of measures on the problem of model selection. One of the key findings in this respect is that the class of Wasserstein distances seems to have the best performance as compared to other distances such as the KL-divergence. In Chapter 3, motivated by the results of Chapter 2, we treat the problem of specifying the risk measure for a particular loss when a set of highly divergent priors concerning the distribution of the loss is available. Starting from the principle that the ``variability" of opinions is not welcome, a fact for which a strong axiomatic framework is provided (see e.g. Klibanoff (2005) and references therein) we introduce the concept of Frechet risk measures, which corresponds to a minimal variance risk measure. Here we view a set of priors as a discrete measure on the space of probability measures and by variance we mean the variance of this discrete probability measure. This requires the use of the concept of Frechet mean. By different metrizations of the space of probability measures we define a variety of Frechet risk measures, the Wasserstein, the Hellinger and the weighted entropic risk measure, and illustrate their use and performance via an example related to the static hedging of derivatives under model uncertainty. In Chapter 4, we consider the problem of numerical calculation of convex risk measures applying techniques from the calculus of variations. Regularization schemes are proposed and the theoretical convergence of the algorithms is considered.

scholarly journals Module Structure on Effect Algebras

2020 ◽  
Author(s):  
Simin Saidi Goraghani ◽  
Rajab Ali Borzooei
Keyword(s):  
Effect Algebra ◽  
Module Structure ◽  
Effect Algebras ◽  
Product Effect

 In this paper, by considering the notions of effect algebra and product effect algebra, we define the concept of effect module. Then we investigate some properties of effect modules, and we present some examples on them. Finally, we introduce some interesting topologies on effect modules.

The Gaussian distribution revisited

1996 ◽  
Vol 28 (2) ◽  
pp. 500-524 ◽  
Author(s):  
Carlos E. Puente ◽  
Miguel M. López ◽  
Jorge E. Pinzón ◽  
José M. Angulo
Keyword(s):  
Fractal Dimension ◽  
Probability Measure ◽  
Gaussian Distribution ◽  
Closed Interval ◽  
Fractal Dimensions ◽  
Cumulative Distribution ◽  
Probability Measures ◽  
New Construction

A new construction of the Gaussian distribution is introduced and proven. The procedure consists of using fractal interpolating functions, with graphs having increasing fractal dimensions, to transform arbitrary continuous probability measures defined over a closed interval. Specifically, let X be any probability measure on the closed interval I with a continuous cumulative distribution. And let fΘ,D:I → R be a deterministic continuous fractal interpolating function, as introduced by Barnsley (1986), with parameters Θ and fractal dimension for its graph D. Then, the derived measure Y = fΘ,D(X) tends to a Gaussian for all parameters Θ such that D → 2, for all X. This result illustrates that plane-filling fractal interpolating functions are ‘intrinsically Gaussian'. It explains that close approximations to the Gaussian may be obtained transforming any continuous probability measure via a single nearly-plane filling fractal interpolator.

Properties of implication in effect algebras

2021 ◽  
Vol 71 (3) ◽  
pp. 523-534
Author(s):  
Ivan Chajda ◽  
Helmut Länger
Keyword(s):  
Hilbert Space ◽  
Quantum Mechanics ◽  
State Space ◽  
Effect Algebra ◽  
Modus Ponens ◽  
Effect Algebras ◽  
Algebraic Semantics ◽  
Deductive Systems ◽  
Algebraic Description ◽  
Residuated Poset

Abstract Effect algebras form a formal algebraic description of the structure of the so-called effects in a Hilbert space which serve as an event-state space for effects in quantum mechanics. This is why effect algebras are considered as logics of quantum mechanics, more precisely as an algebraic semantics of these logics. Because every productive logic is equipped with implication, we introduce here such a concept and demonstrate its properties. In particular, we show that this implication is connected with conjunction via a certain “unsharp” residuation which is formulated on the basis of a strict unsharp residuated poset. Though this structure is rather complicated, it can be converted back into an effect algebra and hence it is sound. Further, we study the Modus Ponens rule for this implication by means of so-called deductive systems and finally we study the contraposition law.

The Gaussian distribution revisited

1996 ◽  
Vol 28 (02) ◽  
pp. 500-524 ◽  
Author(s):  
Carlos E. Puente ◽  
Miguel M. López ◽  
Jorge E. Pinzón ◽  
José M. Angulo
Keyword(s):  
Fractal Dimension ◽  
Probability Measure ◽  
Gaussian Distribution ◽  
Closed Interval ◽  
Fractal Dimensions ◽  
Cumulative Distribution ◽  
Probability Measures ◽  
New Construction

A new construction of the Gaussian distribution is introduced and proven. The procedure consists of using fractal interpolating functions, with graphs having increasing fractal dimensions, to transform arbitrary continuous probability measures defined over a closed interval. Specifically, let X be any probability measure on the closed interval I with a continuous cumulative distribution. And let f Θ,D :I → R be a deterministic continuous fractal interpolating function, as introduced by Barnsley (1986), with parameters Θ and fractal dimension for its graph D. Then, the derived measure Y = f Θ,D (X) tends to a Gaussian for all parameters Θ such that D → 2, for all X. This result illustrates that plane-filling fractal interpolating functions are ‘intrinsically Gaussian'. It explains that close approximations to the Gaussian may be obtained transforming any continuous probability measure via a single nearly-plane filling fractal interpolator.

STABLE AND SEMISTABLE PROBABILITY MEASURES ON CONVEX CONE

2014 ◽  
Vol 98 (3) ◽  
pp. 390-406
Author(s):  
NAM BUI QUANG ◽  
PHUC HO DANG
Keyword(s):  
Probability Measure ◽  
Convex Cone ◽  
Closed Subgroup ◽  
Multiplicative Group ◽  
Domain Of Attraction ◽  
Neutral Element ◽  
Probability Measures ◽  
Semistable Probability

The study concerns semistability and stability of probability measures on a convex cone, showing that the set$S(\boldsymbol{{\it\mu}})$of all positive numbers$t>0$such that a given probability measure$\boldsymbol{{\it\mu}}$is$t$-semistable establishes a closed subgroup of the multiplicative group$R^{+}$; semistability and stability exponents of probability measures are positive numbers if and only if the neutral element of the convex cone coincides with the origin; a probability measure is (semi)stable if and only if its domain of (semi-)attraction is not empty; and the domain of attraction of a given stable probability measure coincides with its domain of semi-attraction.

scholarly journals Coexistency on Hilbert Space Effect Algebras and a Characterisation of Its Symmetry Transformations

2020 ◽  
Vol 379 (3) ◽  
pp. 1077-1112 ◽  
Author(s):  
György Pál Gehér ◽  
Peter Šemrl
Keyword(s):  
Hilbert Space ◽  
Effect Algebra ◽  
Mathematical Structure ◽  
Quantum Measurements ◽  
The Other ◽  
Effect Algebras ◽  
Natural Question ◽  
Fuzzy Event ◽  
Space Effect ◽  
Symmetry Transformations

Abstract The Hilbert space effect algebra is a fundamental mathematical structure which is used to describe unsharp quantum measurements in Ludwig’s formulation of quantum mechanics. Each effect represents a quantum (fuzzy) event. The relation of coexistence plays an important role in this theory, as it expresses when two quantum events can be measured together by applying a suitable apparatus. This paper’s first goal is to answer a very natural question about this relation, namely, when two effects are coexistent with exactly the same effects? The other main aim is to describe all automorphisms of the effect algebra with respect to the relation of coexistence. In particular, we will see that they can differ quite a lot from usual standard automorphisms, which appear for instance in Ludwig’s theorem. As a byproduct of our methods we also strengthen a theorem of Molnár.

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