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Case Theory - Europe PMC

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Last Updated: 03 January 2023

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The case against probabilistic inference: a new deterministic theory of 3D visual processing.

The basic question of human vision is how the brain extracts 3D data from inherently ambiguous visual input. This model assumes that independent depth-cue modules derive noisy but statistically accurate estimates of 3D scene parameters that are gathered by a weighted average. Here I argue that the MLE model fails to incorporate crucial psychophysical results and, more importantly, misinterprets the just apparent difference, which is a common measure of stimulus discriminability, to be an estimation of uncertainty. I suggest a new theory called Intrinsic Constraint, which holds that the visual approach does not derive the most accurate interpretation of the display input, but rather, the most consistent interpretation under varying viewing conditions. This target is achieved with the Vector Sum model, which represents individual cue estimates as components of a multi-dimensional vector whose norm determines the overall output.

Source link: https://europepmc.org/article/MED/36511407


The case against probabilistic inference: a new deterministic theory of 3D visual processing

The main issue of human vision is how the brain extracts 3D data from inherently ambiguous visual input. This model assumes that independent depth-cue modules derive noisy but statistically accurate estimates of 3D scene parameters that are added through a weighted average. I suggest a new theory, called Intrinsic Constraint, which claims that the visual system does not provide the most correct interpretation of the image input, but rather, the most consistent interpretation under changing viewing conditions. Individual cue estimates are included in the Vector Sum model, which reflects individual cue estimates as components of a multi-dimensional vector whose norm determines the total output. This essay is part of a conference talk on the subject of "u2018 New approaches to 3D visionu2019".

Source link: https://europepmc.org/article/MED/PMC9745883


Ecological Restoration Strategies for Mountainous Cities Based on Ecological Security Patterns and Circuit Theory: A Case of Central Urban Areas in Chongqing, China.

In the aftermath of rapid urbanization, urban ecosystems are under significant strain. Cities in the mountains are more vulnerable to erosion due to dramatic topography. Ecological security patterns combined with circuit theory can help identify ecological pitfalls and "pinch points" and recommend targeted ecological restoration and protection plans. "key points" in Chongqing's central metropolitan area of Chongqing's central metropolitan region. This paper uses the MSPA model, investment model, MCR system, and Linkage Mapper Tools to map the geographic source regions, eco-corridors, and "key points" to ensure mountainous city growth and sustainable growth. There are 43 ecological sources in Chongqing's central urban area, total area of 986. 56 km2, that have a multi-patch scattering pattern, according to the report. In the barrier point zone, the study revealed 17 sites totaling 24. 20 km of the ecological corridor.

Source link: https://europepmc.org/article/MED/36554384


Analysis and asymptotic theory for nested case-control designs under highly stratified proportional hazards models.

The results can be described as stratified sampling from the event time risk sets and Borgan et al's analysis approach. The consistency and asymptotic normality of the maximum partial likelihood estimator are used in two unique situations, and the results are used to compare the effectiveness of the stratified analysis to unstratified study when the baseline risks can be semi-parametrically modeled in two special cases. Borgan et al. 's method for absolute risk estimation was used by using the stratified sampling scheme of the stratified analysis, as shown by stratified sampling results of the stratified analysis.

Source link: https://europepmc.org/article/MED/36472759

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions