{"id":8675,"date":"2023-07-21T11:16:51","date_gmt":"2023-07-21T11:16:51","guid":{"rendered":"https:\/\/komorebi.ai\/?page_id=8675"},"modified":"2023-09-21T14:12:22","modified_gmt":"2023-09-21T14:12:22","slug":"variationally-inferred-sampling-through-a-refined-bound","status":"publish","type":"page","link":"https:\/\/komorebi.ai\/en\/variationally-inferred-sampling-through-a-refined-bound\/","title":{"rendered":"Variationally Inferred Sampling through a Refined Bound"},"content":{"rendered":"
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Variationally Inferred Sampling through a Refined Bound<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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MDPI – Entropy, 2022<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This study introduces a \u00abrefined variational approximation\u00bb <\/span>framework<\/b>, which enhances the efficiency of <\/span>Bayesian inference in probabilistic models<\/b> by embedding a Markov chain sampler within a variational posterior approximation.\u00a0<\/span><\/p>

Notably easy to implement, it elevates the mixing speed by tuning sampler parameters using automatic differentiation.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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A new framework that enhances Bayesian inference in probabilistic models enhancing the efficiency of decision-making processes in various fields including financial analysis, risk management, machine learning, and logistics.\n<\/p><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Where does it apply?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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It can be applied in <\/span>financial and risk analysis<\/b>, where state-space time series models are particularly useful, data science for machine learning applications like variational autoencoders, and various other sectors that rely on data-informed decision making, such as logistics, energy, healthcare, and technology sectors among others.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Why does it matters?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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It allows for <\/span>efficient <\/b>large-scale <\/span>Bayesian inference<\/b> in probabilistic models, providing a general approach to tuning Markov chain Monte Carlo sampler parameters.<\/span><\/p>

This innovation <\/span>improves <\/b>the <\/span>applicability <\/b>of models, such as state-space time series and variational autoencoders. Furthermore, low-cost, convenient evidence lower bound (ELBO) approximations are an integral part of this achievement.\u00a0<\/span><\/p>

These advancements could potentially optimize Bayesian modeling, thus significantly impacting inference, prediction, and decision-making processes in various fields.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Variationally Inferred Sampling through a Refined Bound<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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MDPI – Entropy, 2022<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tVictor Gallego <\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tChief Research Officer<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Other publications<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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TUN-AI: Tuna biomass estimation with Machine Learning models trained on oceanography and echosounder FAD data<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Assessing the effect of advertising expenditures upon sales. A Bayesian structural time series model<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Personalizing Text-to-Image Generation via Aesthetic Gradients<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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A Machine Learning method, TUN-AI, for accurate tuna biomass estimation, benefiting the fishing industry, fishery management organizations, and conservation efforts.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Accurate sales predictions based on advertising spend benefiting various industries in strategic advertising expenditure, budget allocation, and marketing campaign planning.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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The study introduces Aesthetic Gradients for personalizing diffusion models, advancing user-interactive technology and impacting fields like digital art, design, and marketing.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tPredictive Analytics, Fisheries<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tAdvertising, Sales Forecasting<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tDiffusion Models, User-Guided ML<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Learn More >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Learn More >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Learn More >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Variationally Inferred Sampling through a Refined Bound MDPI – Entropy, 2022 Abstract This study introduces a \u00abrefined variational approximation\u00bb framework, which enhances the efficiency of Bayesian inference in probabilistic models by embedding a Markov chain sampler within a variational posterior approximation. Notably easy to implement, it elevates the mixing speed by tuning sampler parameters using […]<\/p>","protected":false},"author":1,"featured_media":8758,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"yoast_head":"\nVariationally Inferred Sampling through a Refined Bound - Komorebi AI<\/title>\n<meta name=\"description\" content=\"New framework enhances Bayesian inference in probabilistic models, boosting decision-making in financial analysis, risk management, and more.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/komorebi.ai\/en\/variationally-inferred-sampling-through-a-refined-bound\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Variationally Inferred Sampling through a Refined Bound - Komorebi AI\" \/>\n<meta property=\"og:description\" content=\"New framework enhances Bayesian inference in probabilistic models, boosting decision-making in financial analysis, risk management, and more.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/komorebi.ai\/en\/variationally-inferred-sampling-through-a-refined-bound\/\" \/>\n<meta property=\"og:site_name\" content=\"Komorebi AI\" \/>\n<meta property=\"article:modified_time\" content=\"2023-09-21T14:12:22+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/komorebi.ai\/wp-content\/uploads\/2023\/07\/finance-2-scaled.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"1707\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/komorebi.ai\/variationally-inferred-sampling-through-a-refined-bound\/\",\"url\":\"https:\/\/komorebi.ai\/variationally-inferred-sampling-through-a-refined-bound\/\",\"name\":\"Variationally Inferred Sampling through a Refined Bound - 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