J D Bernal (1901–1971) in perspective

Mackay, Alan L.

doi:10.1007/bf02703329

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…The maximum likelihood estimator (MLE) selects the parameter value which gives the observed data the largest possible probability density in the absence of a prior, although the latter can be easily incorporated. For small numbers of samples, the bias of maximum likelihood estimators can be substantial, but for fairly weak regularity conditions it can be considered asymptotically optimal (Mackay 2003). With large numbers of data points, such as in the case of the LOFAR EoR KSP, the bias of the method tends to zero.…”

Section: The Data-model Inversion Methodsmentioning

confidence: 99%

The LOFAR EoR Data Model: (I) Effects of Noise and Instrumental Corruptions on the 21-cm Reionization Signal-Extraction Strategy

Labropoulos,

Koopmans,

Jelic

et al. 2009

Preprint

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A number of experiments are set to measure the 21-cm signal of neutral hydrogen from the Epoch of Reionization (EoR). The common denominator of these experiments are the large data sets produced, contaminated by various instrumental effects, ionospheric distortions, RFI and strong Galactic and extragalactic foregrounds. In this paper, the first in a series, we present the Data Model that will be the basis of the signal analysis for the LOFAR (Low Frequency Array) EoR Key Science Project (LOFAR EoR KSP). Using this data model we simulate realistic visibility data sets over a wide frequency band, taking properly into account all currently known instrumental corruptions (e.g. direction-dependent gains, complex gains, polarization effects, noise, etc). We then apply primary calibration errors to the data in a statistical sense, assuming that the calibration errors are random Gaussian variates at a level consistent with our current knowledge based on observations with the LOFAR Core Station 1. Our aim is to demonstrate how the systematics of an interferometric measurement affect the quality of the calibrated data, how errors correlate and propagate, and in the long run how this can lead to new calibration strategies. We present results of these simulations and the inversion process and extraction procedure. We also discuss some general properties of the coherency matrix and Jones formalism that might prove useful in solving the calibration problem of aperture synthesis arrays. We conclude that even in the presence of realistic noise and instrumental errors, the statistical signature of the EoR signal can be detected by LOFAR with relatively small errors. A detailed study of the statistical properties of our data model and more complex instrumental models will be considered in the future.

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Section: The Data-model Inversion Methodsmentioning

confidence: 99%

The LOFAR EoR Data Model: (I) Effects of Noise and Instrumental Corruptions on the 21-cm Reionization Signal-Extraction Strategy

Labropoulos,

Koopmans,

Jelic

et al. 2009

Preprint

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“…If there are sufficiently strong preference relations to establish extrema for this distribution then they may be assigned extreme values se 0.0 or 1.0. NA is repeatedly asked to provide probability estimates for the offer w that yields the greatest reduction in entropy for the resulting distribution (MacKay, 2003). This continues until NA considers the distribution to be "satisfactory".…”

Section: Estimating P(naacc())mentioning

confidence: 99%

“…NA does make assumptions about: the way in which the integrity of information will decay, preferences that its opponent may have for some deals over others, and conditions that may lead to breakdown. It also assumes that unknown probabilities can be inferred using maximum entropy probabilistic logic (MacKay, 2003) that is based on random worlds (Halpern, 2003). The maximum entropy probability distribution is "the least biased estimate possible on the given information; i.e.…”

Section: Introductionmentioning

confidence: 99%

A Foundation for Informed Negotiation

Debenham¹,

Simoff²

2006

Proceedings of the Eighth International Conference on Enterprise Information Systems

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Approaches to the construction of agents that are to engage in competitive negotiation are often founded on game theory. In such an approach the agents are endowed with utility functions and assumed to be utility optimisers. In practice the utility function is derived in the context of massive uncertainties both in terms of the agent's priorities and of the raw data or information. To address this issue we propose an agent architecture that is founded on information theory, and that manages uncertainty with entropy-based inference. Our negotiating agent engages in multi-issue bilateral negotiation in a dynamic information-rich environment. The agent strives to make informed decisions. The agent may assume that the integrity of some of its information decays with time, and that a negotiation may break down under certain conditions. The agent makes no assumptions about the internals of its opponent-it focuses only on the signals that it receives. It constructs two probability distributions over the set of all deals. First the probability that its opponent will accept a deal, and second that a deal will prove to be acceptable to it in time.

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Conflicts of interest and the self-picture of medicine and scientists

Rose¹

2022

The COVID-19 Pandemic

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J D Bernal (1901–1971) in perspective

Cited by 3 publications

References 3 publications

The LOFAR EoR Data Model: (I) Effects of Noise and Instrumental Corruptions on the 21-cm Reionization Signal-Extraction Strategy

The LOFAR EoR Data Model: (I) Effects of Noise and Instrumental Corruptions on the 21-cm Reionization Signal-Extraction Strategy

A Foundation for Informed Negotiation

Conflicts of interest and the self-picture of medicine and scientists

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