Results 211 to 220 of about 4,564 (243)

Probability via Expectation Measures. [PDF]

Entropy (Basel)
Harremoës P.
europepmc   +1 more source

Derivatives of Markov kernels and their Jordan decomposition

, 2003
Heidergott, B.F., Hordijk, A., Weisshaupt, H.   +2 more
openaire   +1 more source

Fuzzy Observables: from Weak Markov Kernels to Markov Kernels

International Journal of Theoretical Physics, 2023
AbstractWe provide a proof based on transfinite induction that every weak Markov kernel is equivalent to a Markov kernel. We only assume the space where the weak Markov kernel is defined to be second countable and metrizable. That generalizes some previous results where the kernel is required to be defined on a standard Borel space (which is second ...
Roberto Beneduci, Beneduci Roberto
exaly   +3 more sources

Transportation inequalities for Markov kernels and their applications [PDF]

Electronic Journal of Probability, 2021
38 pages.
Fabrice Baudoin, Nathaniel Eldredge
exaly   +5 more sources

Some of the next articles are maybe not open access.

Related searches:

Markov kernels and tribes

Information Sciences, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Anatolij Dvurečenskij
exaly   +3 more sources

The Category of Markov Kernels

Electronic Notes in Theoretical Computer Science, 1999
AbstractMarkov kernels are fundamental objects in probability theory. One can define a category based on Markov kernels which has many of the formal properties of the ordinary category of relations. In the present paper we will examine the categorical properties of Markov kernels and stress the analogies and differences with the category of relations ...
Prakash Panangaden
exaly   +2 more sources

Derivatives of Markov Kernels and Their Jordan Decomposition [PDF]

Journal of Applied Analysis, 2008
Let \((P_\vartheta)_{\vartheta \in \Theta}\) be a parametric family of Markov kernels from a measurable space \((X, \mathcal{X})\) to a locally compact space \(Y\). The family \((P_\vartheta)_{\vartheta \in \Theta}\) is called weakly differentiable at \(\vartheta\) if for any \(x \in X\) there is a finite signed Baire measure \(P'_\vartheta(x, .)\) on \
Heidergott, B.F., Hordijk, A., Weisshaupt, H.   +2 more
exaly   +4 more sources

High-order Markov kernels for intrusion detection

Neurocomputing, 2008
In intrusion detection systems, sequences of system calls executed by running programs can be used as evidence to detect anomalies. Markov chain is often adopted as the model in the detection systems, in which high-order Markov chain model is well suited for the detection, but as the order of the chain increases, the number of parameters of the model ...
Shengfeng Tian, Chuanhuan Yin
exaly   +2 more sources

Positive Operator Valued Measures and Feller Markov kernels [PDF]

Journal of Mathematical Analysis and Applications, 2016
A Positive Operator Valued Measure (POVM) is a map $F:\mathcal{B}(X)\to\mathcal{L}_s^+(\mathcal{H})$ from the Borel $σ$-algebra of a topological space $X$ to the space of positive self-adjoint operators on a Hilbert space $\mathcal{H}$. We assume $X$ to be Hausdorff, locally compact and second countable and prove that a POVM $F$ is commutative if and ...
Roberto Beneduci
exaly   +4 more sources

On contraction properties of Markov kernels

Probability Theory and Related Fields, 2003
The authors study general properties of contractions of Markov kernels without assumptions on the existence of an invariant probability measure. In their previous paper [in: Séminaire de Probabilités XXXIV. Lect. Notes Math. 1729, 1-145 (2000; Zbl 0963.60040)] they have shown that the system is forgetting its initialization without nevertheless ...
Del Moral, P., Ledoux, M., Miclo, L.
openaire   +1 more source