We can write our own functions for greta arrays, using the existing operators and functions. For example, we could define the inverse hyperbolic tangent function for greta arrays like this:. So far, we have created greta arrays representing the variables in our model with prior distributions and created other greta arrays from them and some fixed, independent data. To perform statistical inference on this model, we also need to link it to some observed dependent data.
By comparing the sepal lengths predicted under different parameter values with the observed sepal lengths, we can estimate the most plausible values of those parameters. We do that by defining a likelihood for the observed sepal length data y. In greta we do that with the distribution assignment function:. Now all of the greta arrays making up the model have been created, we need to combine them and set up the model so that we can sample from it, using model :.
When sampling from the model with mcmc those will be the greta arrays for which samples will be returned. Alternatively, we can run model without passing any greta arrays, in which case all of the greta arrays except for data greta arrays in the working environment will be set as the targets for sampling instead.
The greta arrays in your workspace that are used in the model are all represented as nodes shapes with names. These are either data squares; x and y , variables large circles; int , coef , sd or the results of operations small circles; mean. The operations used to create the operation greta arrays are printed on the arrows from the arrays they were made from.
There are also nodes for greta arrays that were implicitly defined in our model. The fourth type of node diamonds represents probability distributions.
These have greta arrays as parameters linked via solid lines , and have other greta arrays as values linked via dashed lines.
Distributions calculate the probability density of the values , given the parameters and their distribution type. Now we have a greta model that will give us the joint density for a candidate set of values, so we can use that to carry out inference on the model.
HMC uses the gradients of the joint density to efficiently explore the set of parameters. If your model is taking a long time whilst in the sampling phase and you want to take a look at the samples. You can stop the sampler e. The sampler will automatically tune itself during the warmup phase, to make it as efficient as possible.
This is often the case when you have lots of data; the more information there is, the more extreme the log probability, and the higher the risk of numerical problems. Payment Management. Stripe is responsible for all in-app transactions. Taxamo takes care of the tax-related stuff.
However, there are a number of digital tax automation services available online, all offering different features catered to different business models. If you want to learn more about them, follow this link for a side-by-side comparison of the most popular tax automation software. Notification Management. Callr also performs user phone number verification and communicates with devices in case of emergency.
We also integrated MailChimp to collect emails of registered users so that they can receive newsletters and other valuable information from Possio.
Geolocation Management. Although the backend is the foundation of development, mobile applications are what people get to personally use and enjoy. We would like to share our experience developing the Possio applications for iOS and Android and walk you through some of their most entertaining parts.
We wrote the iOS application in Swift. As for the functional architecture and logic of data interaction, we went with the Service-Oriented Architecture SOA , which aligns logical functions and creates a single interface even though there can be several implementations. SDWebImage deals with image caching. We observe API calls with a network activity indicator and use custom extensions and structures to take care of error management.
Using native animations helps improve the overall user experience. Also, thanks to our hand-tuned tension, damping, and velocity along with our custom transitions, we were able to fully satisfy all UI requirements.
WebSockets the StarScream Framework allow for real-time data retrieval from trackers. We also implemented a native localization mechanism for Swedish and Norwegian language support, along with a default file storage to cache user filters. The Android application is written in Java and Kotlin. The MVP architecture, being very flexible, works perfectly when developing apps from the ground up. A project developed with MVP is modular, and you can make changes to any component without damaging the whole app.
Crashlytics by Fabric IO tracks crashes, allowing us to quickly fix issues and keep a detailed log. To fulfil the UX requirements, we built a number of customized views based on Material Design guidelines. The Possio tracker app also integrates an interactive UI and custom views to control user inputs, manage Google Maps, and visualize data. In particular, we gained experience with GPS-enabled products, mastered our ability to work with and monitor systems containing large quantities of data, figured out efficient ways to build complex systems with a number of server components, and topped off our knowledge using different architectural patterns for app development.
Projects like Possio prove that SteelKiwi accepts complexity and is able to tackle it. Its Achilles heel? It will probably never be released outside of Korea. Neonode N2 This phone, which uses an open software platform, probably has a snowball's chance in hell of being used outside of the homebrew mobile phone club.
But the 3-inch tall, gesture-controlled phone is also a true harbinger of phones to come. The A. About Gizmodo Advisor Gizmodo Store. By Noah Robischon.
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