computer science

LaplacesDemon

LaplacesDemon is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand LaplacesDemon rather than just read about it. In short: LaplacesDemon is an open-source statistical package that is intended to provide a complete environment for Bayesian inference. LaplacesDemon has been used in numerous fields.

Key takeaways

LaplacesDemon belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect LaplacesDemon to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of LaplacesDemon from memory before moving on to harder problems.

Reference excerpt

LaplacesDemon is an open-source statistical package that is intended to provide a complete environment for Bayesian inference. LaplacesDemon has been used in numerous fields. The user writes their own model specification function and selects a numerical approximation algorithm to update their Bayesian model. Some numerical approximation families of algorithms include Laplace's method (Laplace approximation), numerical integration (iterative quadrature), Markov chain Monte Carlo (MCMC), and variational Bayesian methods. The base package, LaplacesDemon, is written entirely in the R programming language, and is largely self-contained, though it does require the parallel package for high performance computing via parallelism. Big data is also supported. An extension package called LaplacesDemonCpp is in development to provide C++ functionality. The software was named after the concept of Laplace's demon, which refers to a hypothetical being capable of predicting the universe. Pierre-Simon Laplace alluded to this hypothetical being in the introduction to his Philosophical Essay on Probabilities.

See also Bayesian inference PyMC WinBUGS

References

External links All links below are broken. New references are required.

LaplacesDemon: Official website LaplacesDemon at GitHub: Development of LaplacesDemon LaplacesDemonCpp at GitHub: Development of LaplacesDemonCpp plus.google.com/+Bayesian-inference: Software updates have been announced here

Illustrations

Worked examples

Example 1 — a first encounter with LaplacesDemon

Start with the simplest possible case. Write down what LaplacesDemon claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to LaplacesDemon before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about LaplacesDemon ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of LaplacesDemon

In research: LaplacesDemon appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses LaplacesDemon in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: LaplacesDemon is common in secondary-school and first-year university syllabi. It links to neighbouring topics Free Bayesian statistics software, Monte Carlo software, so understanding it makes those chapters shorter.
In everyday life: Look for LaplacesDemon outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “LaplacesDemon” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study LaplacesDemon in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what LaplacesDemon means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain LaplacesDemon out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is LaplacesDemon in simple terms?

LaplacesDemon is an open-source statistical package that is intended to provide a complete environment for Bayesian inference. LaplacesDemon has been used in numerous fields.

Why does LaplacesDemon matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study LaplacesDemon?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on LaplacesDemon.