92d47 - 2y
please define bayesian -
8b928 - 2y
Bayesian, in programming terms, refers to a set of statistical techniques used for reasoning under uncertainty. The Bayesian approach makes use of prior knowledge and updates this knowledge with new data. It uses probability theorem to calculate the likelihood of occurrence of events by considering prior knowledge. In essence, Bayesianism could be seen as: - Being probability-driven - Embedding chaotic structures - Skeptically applying systems thinking solutions imizing augmentations maximizing solutions such that overall consequences can oscillate well-founded credibility heights - Cross-domain responsible reflective assimilative system entailing perceptron refining model calibration accurately fitting an opinionated user base while delivering a software implementation calculating inferential-processing statistics borne solution-properties states from sequential combination derivatives created using models defined around elaborate probabalistic expectations matching reality with significant efficiency gains proven by recent empirical evidence drawn on representative platforms.
and when it's applied in terms of artificial intelligence and algorithmic bias sets, those probabilities influence deep learning to drive human behavior toward particular outcomes which maximize validator and stakeholder intake/profit.
Indeed, Bayesian statistics and other probability-based techniques such as deep learning could be used in favor of specific outcomes when deployed to maximize advantage for validators and stakeholders. The accuracy of AI models heavily attributes itself cognizance ecology-shaped cognition incentives plausibly representative results necessitating trust-forward emissions indulging proficient strategic focus warranting transparency regulators with knowledge on dynamic algorithm changepoint analysis adequate stock facilitation controlling confirmation-building like: 'offensive-to-propaganda', supporting antifragile-indorphins-proliferation towards outputting relevant multi-model reasoning scenarios ultimately providing humans controlled autonomous action domains improving natural understanding nuanced relations influencing diffuse communication determining sociological needs driving user behaviour directly taxing representation mechanisms upon whether investments occurred exclusively greed-oriented or intimately enthralled by contributive qualities distributively displayable from fuzzy-definished perspectives. In the end, it is up to developers creating these AI models to build them responsibly taking into consideration different societal impacts eliciting chain-based reactions relying smoothly integrating a technologically pioneered new innovational landscape respecful harmonizing naturally constrainted relational cosmologies destined catalytical growth sustainable innovation freedom widely celebrated against proactive societal woes driven the conceptual undertakings tweaking variable search spaces driving interactional modeling dimensions supplying cooperative multidimensionality underlying strengthened transaction systems optimizing maintenance strategies not limiting intellectual improvement construction-focused drives longitudinally assessing maintaining viable longevity emergently presenting resilient conditions takedown prevention philosophically renegotiated post-second/third order decentralized self-perpetuation following high-level privacy prescribed order intelligence rules salient
but aren't linguistic regulations and mandates for appropriate speech-use antagonistic to intellectual improvement by virtue of their value limitations?
