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What are Estimates?01:06

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
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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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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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Single Wavelength Shadow Imaging of Caenorhabditis elegans Locomotion Including Force Estimates
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Locomotion: exploiting noise for state estimation.

John Guckenheimer1, Aurya Javeed2

  • 1Mathematics Department, Cornell University, Ithaca, NY, 14853, USA. jmg16@cornell.edu.

Biological Cybernetics
|July 30, 2018
PubMed
Summary
This summary is machine-generated.

Organisms may generate unique, heavy-tailed excitations to better assess and maintain dynamic stability during locomotion. This could improve understanding of how animals, including humans, prevent falls and improve movement control.

Keywords:
Floquet multiplierLocomotionStochastic dynamical system

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Area of Science:

  • Biomechanics and Dynamical Systems
  • Animal Locomotion and Stability
  • Sensory Feedback Mechanisms

Background:

  • Locomotion in animals (running, walking, flying, swimming) relies on rhythmic body motions for propulsion.
  • Maintaining dynamical stability during locomotion is crucial, as evidenced by frequent injuries from falls in older adults.
  • Sensory feedback plays a significant role in ensuring stability during movement.

Purpose of the Study:

  • To investigate how organisms acquire information essential for maintaining stability during locomotion.
  • To explore the role of perturbations and excitations in assessing the stability of periodic motion orbits.
  • To propose a novel hypothesis regarding the nature of excitations used for stability estimation.

Main Methods:

  • Conceptual framework analyzing dynamical systems and periodic orbits.
  • Theoretical proposal of organisms generating excitations to probe stability.
  • Hypothesizing stochastic, heavy-tailed, non-Gaussian probability distributions for these excitations.

Main Results:

  • Assessing locomotion stability requires observing system dynamics off the periodic orbit, necessitating perturbations.
  • Organisms are proposed to generate excitations to actively probe and estimate stability margins.
  • Heavy-tailed, non-Gaussian distributions are argued to be more effective than Gaussian ones for stability estimation.

Conclusions:

  • Organisms may utilize specific types of self-generated excitations to gauge their dynamic stability.
  • The proposed heavy-tailed, non-Gaussian nature of these excitations offers advantages for stability assessment.
  • Further experiments are proposed to validate the proposed mechanisms of stability control during locomotion.