Show HN: Deterministic objective Bayesian inference for spatial models [pdf] To give some context, objective Bayesian inference refers to Bayesian analysis (i.e. integrating over the parameter space) using a prior that is design to represent "minimal information" (see [1], [2], and [3] for an overview). Particularly in cases where a model's likelihood function is not strongly peaked about a point, objective Bayesian inference can give better results than methods based off of point estimates like Maximum Likelihood [4]. Reference priors provides a general approach to construct so-called noninformative priors that are suitable for Objective Bayesian analysis ([5], [6]). The approach takes a practical viewpoint of noninformative priors and looks to build priors that are both tractable and provide good performance on frequentist coverage simulations. See Section 2 for a description of how the process and frequentist simulations work and [7] for examples with some basic models (e.g. why 1/σ^2 is the noninformative prior for the variance of normally distributed data with known mean). [8] was the first to develop reference priors for Gaussian processes models, and [9] extended the work to handle Gaussian Processes with noise (or nugget effects). The project I'm working on provides software and algorithms to do deterministic inference using the prior from [9]. Typically, such inference has been done using MCMC sampling algorithms; but my belief is that deterministic algorithms can give results that are more consistent, less sensitive to parameter tweaking, and more efficient, at the expense of some engineering cost and loss of generality. For an example of how the algorithms work on a real-world data set of zinc measurements in a flood plain along the Meuse river [10], see https://ift.tt/PFwH7mU... References [1]: https://ift.tt/ytLSzwl [2]: https://ift.tt/toDP94S... [3]: https://ift.tt/5hl6piL [4]: https://ift.tt/LtUNADR... [5]: https://ift.tt/x9IUJhs... [6]: https://ift.tt/FToUNhQ [7]: https://ift.tt/VWLswPc... [8]: https://ift.tt/7SygdIt... [9]: https://ift.tt/qShuT3G... [10]: https://ift.tt/RNYvrKB... https://ift.tt/oeucsfP May 10, 2023 at 09:07PM
Show HN: Launch VM workloads securely and instantaneously, without VMs Hello HN! We've been working on a new hypervisor https://kwarantine.xyz that can run strongly isolated containers. This is still a WIP, but we wanted to give the community an idea about our approach, its benefits, and various use cases it unlocks. Today, VMs are used to host containers, and make up for the lack of strong security as well as kernel isolation in containers. This work adds this missing security piece in containers. We plan on launching a free private beta soon. Meanwhile, we'd deeply appreciate any feedback, and happy to answer any questions here or on our slack channel. Thanks! April 29, 2021 at 07:50AM
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