Monte Carlo Methods

Kalos, M. H.; Whitlock, Paula A.

doi:10.1002/9783527626212

Cited by 722 publications

(672 citation statements)

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“…Generally, the outcome of a random event can be mapped into a numerical value. Monte Carlo simulation involves deliberate usage of random numbers in a calculation that has the structure of a stochastic process, which comprises a sequence of states whose evolution is determined by random events according to Kalos and Whitlock (2008).…”

Section: Review Of Monte Carlo Simulation Application In Optical Netwmentioning

confidence: 99%

Estimation of Optical Access Network Bandwidth Demand Using Monte Carlo Simulation

Mikavica¹,

Radojicic²,

Kostic-Ljubisavljevic³

2015

IJTTE

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Continuous forecast of bandwidth demand in order to achieve a proper network planning and dimensioning is one of the key challenges that network operators need to deal with. The importance of this issue is highlighted with the necessity of proper decision making regarding capital investments and the permanent increase in traffic volume. Under-investing in network resources lead to low network performances and hence dissatisfied customers, while over-investing may cause over-dimensioned capacities which leads to lower resource utilization and opportunity losses. In that perspective, it is essential to determine whether current network capacities satisfy bandwidth requirements. If it does, determining relevant time period is of great significance. As the bandwidth demand increases by the development of new services, the existing broadband networks need to be upgraded. The growing bottleneck can be solved rolling out optical access network. These networks are characterized by greater bandwidth supply enhancement compared to bandwidth demand. That fact emphasizes the need of appropriate bandwidth forecast. This paper presents model for in optical access networks future bandwidth demand and network resource utilisation estimation. In order to appropriately address valid demand of all households in a given area, Monte Carlo simulation is applied.

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Section: Review Of Monte Carlo Simulation Application In Optical Netwmentioning

confidence: 99%

Estimation of Optical Access Network Bandwidth Demand Using Monte Carlo Simulation

Mikavica¹,

Radojicic²,

Kostic-Ljubisavljevic³

2015

IJTTE

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“…In other words, at each level of recursion ray tracing algorithm can split ray into components for reflection, transmission (refraction) or in the case of participating media absorption and in particular cases emission, where a sum of radiosity values of the component part must be equal to incident radiosity. Splitting is controlled either by deterministic or stochastic 1 -Monte Carlo methods [35] [36] [37], essential for all path tracing methods. In a typical Monte Carlo numerical algorithm, random draws following given distributions define a chain of local events characterising the global event and leading to a final state.…”

Section: Stochastic (Monte Carlo) Based Methods -Ray Tracingmentioning

confidence: 99%

State-of-the-art review of solar design tools and methods for assessing daylighting and solar potential for building-integrated photovoltaics

Jakica

2018

Renewable and Sustainable Energy Reviews

129

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Solar design can take many different forms across disciplines with different methodologies and goals, ranging from acquiring architectural visual effects to assessing illumination for daylighting and solar radiation potential on building surfaces for PV implementation. Furthermore, a capability of solar design methodologies and tools to accurately and time efficiently simulate light phenomena can greatly influence performance results and design decisions. This is especially important in complex cases such as dense urban settings with the significant surface shadowing, and vertical facades including daylighting devices and photovoltaics. Consequently, choosing a suitable approach and tool for each design phase is essential for achieving unique design and performance goals. This paper was carried out within the framework of IEA-PVPS Task 15 -BIPV and it aims to facilitate this decision for all parties involved in solar design process. Here presented, is an overview of almost 200 solar design tools, analyzing their numerous features regarding accuracy, complexity, scale, computation speed, representation as well as building design process integration in about 50 2D/3D, CAD/CAM and BIM software environments. Furthermore, tools from various fields have been analysed in a broad interdisciplinary context of solar design with a particular attention for being used for Daylighting and Building-Integrated Photovoltaics (BIPV) purposes. This approach should open many new perspectives on a potentially wider multidisciplinary usage and interpretation of solar design tools, sometimes well beyond their initial scope of work.

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“…In importance sampling one wants to find a weak approximation of the pdf, f , of a continuous random variable (called the target pdf), by generating weighted samples from a known density f 0 (called the importance function), see e.g. [1,33,34]. The weight of the sample X j (obtained by sampling f 0 ),…”

Section: Implicit Particle Filtersmentioning

confidence: 99%

A Survey of Implicit Particle Filters for Data Assimilation

Chorin

Morzfeld

2013

State-Space Models

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The implicit particle filter is a sequential Monte Carlo method for data assimilation. The idea is to focus the particles onto the high probability regions of the target probability density function (pdf) so that the number of particles required for a good approximation of this pdf remains manageable, even if the dimension of the state space is large. We explain how this idea is implemented, discuss special cases of practical importance, and work out the relations of the implicit particle filter with other data assimilation methods. We illustrate the theory with four examples.

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Monte Carlo Methods

Cited by 722 publications

References 0 publications

Estimation of Optical Access Network Bandwidth Demand Using Monte Carlo Simulation

Estimation of Optical Access Network Bandwidth Demand Using Monte Carlo Simulation

State-of-the-art review of solar design tools and methods for assessing daylighting and solar potential for building-integrated photovoltaics

A Survey of Implicit Particle Filters for Data Assimilation

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