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Related Concept Videos

Approximate Integration01:24

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In many practical and theoretical contexts, the exact value of a definite integral may be inaccessible. This limitation typically arises when the antiderivative of a function is either unknown or cannot be expressed in a closed mathematical form. Alternatively, it can occur when a function is defined not by a formula but by a finite set of empirical data points, such as those collected during experiments. In these cases, approximate integration techniques provide a valuable solution.One of the...
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Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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al3c: high-performance software for parameter inference using Approximate Bayesian Computation.

Alexander H Stram1, Paul Marjoram2, Gary K Chen2

  • 1Cancer Center - Research, USC, Los Angeles, CA 90089, USA and.

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Summary
This summary is machine-generated.

We introduce al3c, a C++ framework for parallel Approximate Bayesian Computation (ABC) Sequential Monte Carlo (SMC) methods. This tool enables scalable ABC-SMC in parallel environments with minimal programming effort, addressing current software limitations.

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Area of Science:

  • Computational Biology
  • Statistical Inference
  • Bioinformatics

Background:

  • Approximate Bayesian Computation (ABC) algorithms are crucial for parameter inference.
  • Monte Carlo rejection sampling, a core ABC component, is inefficient and difficult to scale.
  • Existing ABC Sequential Monte Carlo (SMC) methods are not easily parallelizable, limiting scalability in computational environments.

Purpose of the Study:

  • To develop a computationally efficient and scalable framework for parallel ABC-SMC.
  • To abstract users from complex parallel programming and ABC-SMC algorithm details.
  • To enable users to scale ABC-SMC for their specific applications with minimal overhead.

Main Methods:

  • al3c is a C++ framework designed for parallel ABC-SMC implementation.
  • It requires users to define essential functions like simulation models and prior distributions.
  • The framework is available as static binaries for Linux and OS-X, using XML configuration and C++ plug-in templates.

Main Results:

  • al3c facilitates the parallel implementation of ABC-SMC.
  • Users can achieve scalable ABC-SMC with minimal programming effort.
  • The framework abstracts programming complexities, allowing focus on the scientific application.

Conclusions:

  • al3c provides a scalable solution for parallel ABC-SMC.
  • It lowers the barrier for researchers to utilize parallel computing for Bayesian inference.
  • The framework enhances the efficiency and applicability of ABC-SMC in computational research.