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Uncertainty


Deep Understanding of Discriminative and Generative Models

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In today's world, Machine learning becomes one of the popular and exciting fields of study that gives machines the ability to learn and become more accurate at predicting outcomes for the unseen data i.e, not seen the data in prior. The ideas in Machine learning overlaps and receives from Artificial Intelligence and many other related technologies. Today, machine learning is evolved from Pattern Recognition and the concept that computers can learn without being explicitly programmed to performing specific tasks. We can use the Machine Learning algorithms(e.g, Machine learning models can be classified into two types of models – Discriminative and Generative models.


Algorithms for decision making: excellent free download book from MIT - DataScienceCentral.com

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MIT press provides another excellent book in creative commons. I plan to buy it and I recommend you do. This book provides a broad introduction to algorithms for decision making under uncertainty. An agent is an entity that acts based on observations of its environment. The interaction between the agent and the environment follows an observe-act cycle or loop.


Conditional Independence

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When it comes to probability theory we all would have heard of joint distribution, marginal distribution, independence etc. In this article I will focus my attention onto independence specially conditional independence. In others words if the happening of event A doesn't affect the probability of event B happening, both events are said to be independent. From the view of information theory it can be interpreted as: if knowing A doesn't provide any additional information about B, then A and B are said to be independent. These are the different interpretations for the concept of independence.


Measuring dependence in the Wasserstein distance for Bayesian nonparametric models

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Bayesian nonparametric (BNP) models are a prominent tool for performing flexible inference with a natural quantification of uncertainty. Notable examples for \(T\) include normalization for random probabilities (Regazzini et al., 2003), kernel mixtures for densities (Lo, 1984) and for hazards (Dykstra and Laud, 1981; James, 2005), exponential transformations for survival functions (Doksum, 1974) and cumulative transformations for cumulative hazards (Hjort, 1990). Very often, though, the data presents some structural heterogeneity one should carefully take into account, especially when analyzing data from different sources that are related in some way. For instance this happens in the study of clinical trials of a COVID-19 vaccine in different countries or when understanding the effects of a certain policy adopted by multiple regions. In these cases, besides modeling heterogeneity, one further aims at introducing some probabilistic mechanism that allows for borrowing information across different studies.


When to use Bayesian

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Bayesian statistics is all about belief. We have some prior belief about the true model, and we combine that with the likelihood of our data to get our posterior belief about the true model. In some cases, we have knowledge about our domain before we see any of the data. Bayesian inference provides a straightforward way to encode that belief into a prior probability distribution. For example, say I am an economist predicting the effects of interest rates on tech stock price changes.


Learning Data Science from Real-World Projects

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Mixed-integer programming saves the day. Taking a cue from consumer supply chains and the data-driven advances that have revolutionized them in recent decades, Gabe Verzino walks us through a scheduling program that would empower both patients and healthcare providers to use their time more efficiently. Bayes' Theorem might sound, well, theoretical. As Khuyen Tran shows in her recent tutorial (based on the traffic patterns of her own website), it can also be a powerful tool for detecting and analyzing change points in your data. The road to the perfect shot of espresso passes through a lot of data.


Eindhoven, Netherlands - Assistant Professor Job in AI, Machine learning

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We seek to appoint an assistant or associate professor in the general area of Uncertainty in AI, who is passionate about research as well as teaching. We particularly welcome excellent candidates that can contribute to foundational aspects of AI and machine learning.The successful candidate will help with developing and/or delivering courses in DAI cluster, such as Foundations of AI, Explainable AI, Text Mining, Reinforcement Learning, Uncertainty Representation and Reasoning, and Generative Models, and will supervise students at all levels. The working language in the department and across the university is English. An important aspect of TU/e's vision on education is that research and education go hand in hand, both at Bachelor and Master level.Next to your research, education is an important part of your job.The TU/e helps its scientific staff to further develop their teaching skills by offering a training program that leads to an official teaching certification from Dutch Universities (Basic Teaching Qualification).Furthermore, you should have: Are you inspired to work for the exciting Department of Mathematics and Computer Science at TU Eindhoven? We're looking for you as our new faculty member to expand our academic staff in the Data and AI cluster.


Bayesian Inference in Python

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Life is uncertain, and statistics can help us quantify certainty in this uncertain world by applying the concepts of probability and inference.


Generative Modeling by Estimating Gradients of the Data Distribution

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This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood computation, and inverse problem solving without re-training models. In this blog post, we will show you in more detail the intuition, basic concepts, and potential applications of score-based generative models. Existing generative modeling techniques can largely be grouped into two categories based on how they represent probability distributions. Likelihood-based models and implicit generative models, however, both have significant limitations.


Bean Machine: Composable, Fast Probabilistic Inference on PyTorch

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Today, we're excited to announce an early beta release of Bean Machine, a PyTorch-based probabilistic programming system that makes it easy to represent and to learn about uncertainties in the machine learning models that we work with every day. Bean Machine enables you to develop domain-specific probabilistic models, and automatically learn about unobserved properties of the model with automatic, uncertainty-aware learning algorithms. Though powerful, probabilistic modeling does take some getting used to. If this is your first exposure to the topic, we welcome you to check out a short overview of the concept in the Fabulous Adventures in Coding blog. We on the Bean Machine development team believe that the usability of a system forms the bedrock for its success, and we've taken care to center Bean Machine's design around a declarative philosophy within the PyTorch ecosystem.