Example of Methodology and Computing in Applied Probability format
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Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format
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Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format Example of Methodology and Computing in Applied Probability format
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open access Open Access

Methodology and Computing in Applied Probability — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Mathematics (all) #128 of 378 down down by 51 ranks
Statistics and Probability #124 of 239 down down by 42 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 275 Published Papers | 426 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 20/07/2020
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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

0.809

8% from 2018

Impact factor for Methodology and Computing in Applied Probability from 2016 - 2019
Year Value
2019 0.809
2018 0.746
2017 0.885
2016 0.965
graph view Graph view
table view Table view

1.5

6% from 2019

CiteRatio for Methodology and Computing in Applied Probability from 2016 - 2020
Year Value
2020 1.5
2019 1.6
2018 1.6
2017 1.8
2016 1.8
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 8% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has decreased by 6% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.481

15% from 2019

SJR for Methodology and Computing in Applied Probability from 2016 - 2020
Year Value
2020 0.481
2019 0.569
2018 0.426
2017 0.515
2016 0.685
graph view Graph view
table view Table view

1.113

2% from 2019

SNIP for Methodology and Computing in Applied Probability from 2016 - 2020
Year Value
2020 1.113
2019 1.13
2018 0.911
2017 1.12
2016 1.266
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 15% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 2% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Methodology and Computing in Applied Probability

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Springer

Methodology and Computing in Applied Probability

Methodology and Computing in Applied Probability will publish high quality research and review articles in the areas of applied probability that emphasize methodology and computing. Of special interest are articles in important areas of applications that include detailed case ...... Read More

Mathematics

i
Last updated on
20 Jul 2020
i
ISSN
1387-5841
i
Impact Factor
High - 1.135
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM (1982) Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys Rev B 25(7):4515_x0015_ 4532, URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

Journal Article DOI: 10.1023/A:1010091220143
The Cross-Entropy Method for Combinatorial and Continuous Optimization

Abstract:

We present a new and fast method, called the cross-entropy method, for finding the optimal solution of combinatorial and continuous nonconvex optimization problems with convex bounded domains. To find the optimal solution we solve a sequence of simple auxiliary smooth optimization problems based on Kullback-Leibler cross-entr... We present a new and fast method, called the cross-entropy method, for finding the optimal solution of combinatorial and continuous nonconvex optimization problems with convex bounded domains. To find the optimal solution we solve a sequence of simple auxiliary smooth optimization problems based on Kullback-Leibler cross-entropy, importance sampling, Markov chain and Boltzmann distribution. We use importance sampling as an important ingredient for adaptive adjustment of the temperature in the Boltzmann distribution and use Kullback-Leibler cross-entropy to find the optimal solution. In fact, we use the mode of a unimodal importance sampling distribution, like the mode of beta distribution, as an estimate of the optimal solution for continuous optimization and Markov chains approach for combinatorial optimization. In the later case we show almost surely convergence of our algorithm to the optimal solution. Supporting numerical results for both continuous and combinatorial optimization problems are given as well. Our empirical studies suggest that the cross-entropy method has polynomial in the size of the problem running time complexity. read more read less

Topics:

Optimization problem (68%)68% related to the paper, Cross-entropy method (68%)68% related to the paper, Continuous optimization (66%)66% related to the paper, Combinatorial optimization (64%)64% related to the paper, Random optimization (63%)63% related to the paper
902 Citations
Journal Article DOI: 10.1023/A:1023562417138
Langevin Diffusions and Metropolis-Hastings Algorithms
Gareth O. Roberts1, O. Stramer2

Abstract:

We consider a class of Langevin diffusions with state-dependent volatility. The volatility of the diffusion is chosen so as to make the stationary distribution of the diffusion with respect to its natural clock, a heated version of the stationary density of interest. The motivation behind this construction is the desire to co... We consider a class of Langevin diffusions with state-dependent volatility. The volatility of the diffusion is chosen so as to make the stationary distribution of the diffusion with respect to its natural clock, a heated version of the stationary density of interest. The motivation behind this construction is the desire to construct uniformly ergodic diffusions with required stationary densities. Discrete time algorithms constructed by Hastings accept reject mechanisms are constructed from discretisations of the algorithms, and the properties of these algorithms are investigated. read more read less

Topics:

Stationary distribution (57%)57% related to the paper, Metropolis–Hastings algorithm (50%)50% related to the paper
295 Citations
open accessOpen access Journal Article DOI: 10.1007/S11009-006-9753-0
The Cross-Entropy Method for Continuous Multi-Extremal Optimization
Dirk P. Kroese1, Sergey Porotsky, Reuven Y. Rubinstein2

Abstract:

In recent years, the cross-entropy method has been successfully applied to a wide range of discrete optimization tasks. In this paper we consider the cross-entropy method in the context of continuous optimization. We demonstrate the effectiveness of the cross-entropy method for solving difficult continuous multi-extremal opti... In recent years, the cross-entropy method has been successfully applied to a wide range of discrete optimization tasks. In this paper we consider the cross-entropy method in the context of continuous optimization. We demonstrate the effectiveness of the cross-entropy method for solving difficult continuous multi-extremal optimization problems, including those with non-linear constraints. read more read less

Topics:

Continuous optimization (78%)78% related to the paper, Discrete optimization (72%)72% related to the paper, Optimization problem (67%)67% related to the paper, Random optimization (67%)67% related to the paper, Test functions for optimization (64%)64% related to the paper
View PDF
251 Citations
Journal Article DOI: 10.1007/S11009-006-8550-0
An Adaptive Version for the Metropolis Adjusted Langevin Algorithm with a Truncated Drift
Yves F. Atchadé1

Abstract:

This paper extends some adaptive schemes that have been developed for the Random Walk Metropolis algorithm to more general versions of the Metropolis-Hastings (MH) algorithm, particularly to the Metropolis Adjusted Langevin algorithm of Roberts and Tweedie (1996). Our simulations show that the adaptation drastically improves ... This paper extends some adaptive schemes that have been developed for the Random Walk Metropolis algorithm to more general versions of the Metropolis-Hastings (MH) algorithm, particularly to the Metropolis Adjusted Langevin algorithm of Roberts and Tweedie (1996). Our simulations show that the adaptation drastically improves the performance of such MH algorithms. We study the convergence of the algorithm. Our proves are based on a new approach to the analysis of stochastic approximation algorithms based on mixingales theory. read more read less

Topics:

Metropolis light transport (61%)61% related to the paper, Multiple-try Metropolis (60%)60% related to the paper, Rejection sampling (57%)57% related to the paper, Metropolis–Hastings algorithm (53%)53% related to the paper, Stochastic approximation (52%)52% related to the paper
View PDF
195 Citations
Journal Article DOI: 10.1007/S11009-011-9226-Y
Uniform Asymptotics for the Finite-Time Ruin Probability of a Dependent Risk Model with a Constant Interest Rate
Kaiyong Wang1, Kaiyong Wang2, Yuebao Wang2, Qingwu Gao2, Qingwu Gao3

Abstract:

This paper gives an asymptotically equivalent formula for the finite-time ruin probability of a nonstandard risk model with a constant interest rate, in which both claim sizes and inter-arrival times follow a certain dependence structure. This new dependence structure allows the underlying random variables to be either positi... This paper gives an asymptotically equivalent formula for the finite-time ruin probability of a nonstandard risk model with a constant interest rate, in which both claim sizes and inter-arrival times follow a certain dependence structure. This new dependence structure allows the underlying random variables to be either positively or negatively dependent. The obtained asymptotics hold uniformly in a finite time interval. Especially, in the renewal risk model the uniform asymptotics of the finite-time ruin probability for all times have been given. The obtained results have extended and improved some corresponding results. read more read less

Topics:

Ruin theory (70%)70% related to the paper, Random variable (55%)55% related to the paper, Constant (mathematics) (51%)51% related to the paper
173 Citations
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13. What is Sherpa RoMEO Archiving Policy for Methodology and Computing in Applied Probability?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Methodology and Computing in Applied Probability. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Methodology and Computing in Applied Probability?

The 5 most common citation types in order of usage for Methodology and Computing in Applied Probability are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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