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Basic website
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‎.gitignore‎

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# Binaries for programs and plugins
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*.exe
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*.exe~
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*.dll
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*.so
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*.dylib
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public/*
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# Test binary, build with `go test -c`
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*.test
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# Output of the go coverage tool, specifically when used with LiteIDE
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*.out
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\.vscode/

‎.gitmodules‎

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[submodule "themes/natrium"]
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path = themes/natrium
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url = https://github.com/mobybit/hugo-natrium-theme.git

‎README.md‎

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# website
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# Website
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PINTS website (https://www.cs.ox.ac.uk/projects/PINTS/)

‎archetypes/default.md‎

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---
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title: "{{ replace .TranslationBaseName "-" " " | title }}"
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date: {{ .Date }}
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draft: true
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---
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‎config.toml‎

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baseurl = "https://www.cs.ox.ac.uk/projects/PINTS/"
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languageCode = "en-gb"
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title = "Parametrising biological models with PINTS"
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theme = "natrium"
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preserveTaxonomyNames = true
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rssLimit = 10
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paginate = 10
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pygmentsUseClasses = true
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pygmentsCodefences = true
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googleAnalytics = "UA-48149829-6"
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enableGitInfo = true
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[taxonomies]
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category = "categories"
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tag = "tags"
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[permalinks]
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post = "/:slug/"
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page = "/:slug/"
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[[menu.main]]
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name = "Functional testing"
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url = "/functional-testing/"
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weight = -150
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[[menu.main]]
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name = "People"
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url = "/people/"
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weight = -140
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[[menu.main]]
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name = "Contact"
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url = "/contact/"
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weight = -110
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[params]
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description = ""
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[params.logo]
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url = "pints-logo.svg"
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alt = "PINTS"

‎content/_index.md‎

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---
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title: "Parametrising biological models with PINTS"
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date: 2018-05-02T16:20:41+01:00
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draft: false
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---
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# Parametrising biological models with PINTS
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Parameter estimation is the process of finding a set of parameter values that minimise the mismatch between reality and a model's predictions.
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In the complex non-linear problems that arise in computational biology this is not a trivial task! Further problems arise when we do find a set of values: how certain can we be that they're the best possible values?
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And does a single "best" parameter set even exist, or should we be thinking about a plausible distribution of values?
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PINTS (Probabilistic Inference in Noisy Time-Series) is a software package that brings together optimisation and Bayesian inference methods, allowing us to investigate these problems.
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By using PINTS' state-of-the-art methods, we aim to answer important questions like "How well can this model reproduce our experimental data?", "Which of these models will give us the best predictions?", and "Which experiments should we perform to get the narrowest error bounds on our estimates?"
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PINTS is being developed in a fully open spirit, at: https://github.com/pints-team

‎content/page/contact.md‎

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---
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title: "Contact"
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date: 2019-01-11T17:14:39Z
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draft: false
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---
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If you have any questions, please email us at <pints@maillist.ox.ac.uk>.

‎content/page/functional-testing.md‎

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---
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title: "Functional testing"
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date: "2018-02-09"
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---
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# Coming soon
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Watch this space.

‎content/page/people.md‎

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---
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title: "People"
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date: 2018-05-02T16:25:22+01:00
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draft: false
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---
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{{% section-div %}}
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## [David Gavaghan](/people/David.Gavaghan/)
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![Personal photo - David Gavaghan](photo_david_gavaghan.jpg "David Gavaghan")
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David Gavaghan is Professor of Computational Biology in the Department of Computer Science.
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His research is interdisciplinary and involves the application of mathematical and computational techniques to problems in the biomedical sciences.
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He has a long-standing interest in computational science, and in the development of open source software tools and of computational infrastructures to support the research scientist.
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David currently serves on the advisory board of the Software Sustainability Institute (SSI).
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{{% /section-div %}}
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{{% section-div %}}

‎content/page/site-notice.md‎

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---
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date: "2018-02-09"
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title: "Site notice"
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slug: "site-notice"
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---
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### Website Design
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This website uses [Hugo](https://gohugo.io/) static website generator ({{% hugo-version %}}), with the [Natrium](https://github.com/mobybit/hugo-natrium-theme) theme.
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### Copyright
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This website uses the [Roboto](https://fonts.google.com/specimen/Roboto) typeface (Apache 2.0 licence).

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