tanhloss

For <code>k</code> &gt; 0, defines Julia code that defines the loss function,
$$L(\hat{\theta}, \theta) = \tanh\left(\frac{|\hat{\theta} - \theta|}{k}\right),$$
which approximates the 0-1 loss as <code>k</code> tends to zero.
The resulting string is intended to be used in the function <code>train</code>, but can also be converted to a callable function using <code>juliaEval</code>.

An 'R' interface to the 'Julia' package 'NeuralEstimators.jl'. The package facilitates the user-friendly development of neural point estimators, which are neural networks that map data to a point summary of the posterior distribution. These estimators are likelihood-free and amortised, in the sense that, after an initial setup cost, inference from observed data can be made in a fraction of the time required by conventional approaches; see Sainsbury-Dale, Zammit-Mangion, and Huser (2024) <doi:10.1080/00031305.2023.2249522> for further details and an accessible introduction. The package also enables the construction of neural networks that approximate the likelihood-to-evidence ratio in an amortised manner, allowing one to perform inference based on the likelihood function or the entire posterior distribution; see Zammit-Mangion, Sainsbury-Dale, and Huser (2024, Sec. 5.2) <doi:10.48550/arXiv.2404.12484>, and the references therein. The package accommodates any model for which simulation is feasible by allowing the user to implicitly define their model through simulated data.

Matthew Sainsbury-Dale

NeuralEstimators

Likelihood-Free Parameter Estimation using Neural Networks

tanhloss function

<dl><dt>k</dt>
<dd>Positive numeric value that controls the smoothness of the approximation.</dd></dl>

Arguments

tanhloss — tanhloss

<dl>

<dt>k</dt>
<dd>Positive numeric value that controls the smoothness of the approximation.</dd>

</dl>

tanhloss: tanhloss

Description

Usage

Value

Arguments