read_file

Incorporates Approximate Bayesian Computation to get a posterior distribution and to select a model optimal parameter for an observation point. Additionally, the meta-sampling heuristic algorithm is realized for parameter estimation, which requires no model runs and is dimension-independent. A sampling scheme is also presented that allows model runs and uses the meta-sampling for point generation. A predictor is realized as the meta-sampling for the model output. All the algorithms leverage a machine learning method utilizing the maxima weighted Isolation Kernel approach, or 'MaxWiK'. The method involves transforming raw data to a Hilbert space (mapping) and measuring the similarity between simulated points and the maxima weighted Isolation Kernel mapping corresponding to the observation point. Comprehensive details of the methodology can be found in the papers Iurii Nagornov (2024) <doi:10.1007/978-3-031-66431-1_16> and Iurii Nagornov (2023) <doi:10.1007/978-3-031-29168-5_18>.

Yuri Nagornov

MaxWiK

Machine Learning Method Based on Isolation Kernel Mean Embedding

read_file function

<dl><dt>file_name</dt>
<dd>Name of file to read</dd>
<dt>stringsAsFactors</dt>
<dd>Parameter for read.table function, by default stringsAsFactors = FALSE</dd>
<dt>header</dt>
<dd>Logical type to read or do not read head of a file</dd></dl>

Arguments

Function to read file — read_file

<dl>

<dt>file_name</dt>
<dd>Name of file to read</dd>


<dt>stringsAsFactors</dt>
<dd>Parameter for read.table function, by default stringsAsFactors = FALSE</dd>


<dt>header</dt>
<dd>Logical type to read or do not read head of a file</dd>

</dl>

read_file: Function to read file

Description

Usage

Value

Arguments

Examples