https://www.yomiuri.co.jp/culture/book/column/20230411-OYT8T50001/ に書かれた、太刀川氏による文章をもとにChatGPTと会話した記録
次の文章に誤りもしくは不自然な点があれば指摘してください: まず気候変動の緩和とは、二酸化炭素などの温室効果ガスを削減して気候変動を和らげる戦略だ。これは具体的でわかりやすい。一方で気候変動への適応は、起こった気候の変化に社会を合わせる戦略だ。うむ…わかる気もするが、一体どうすれば適応できるのか。生活の全てが適応の手段なので、指標も測りづらい。適応策はこの複雑さ故に、緩和策ほどには進んでいない。
文章に誤りや不自然な点は見受けられませんが、一部表現をより明確にする提案があります。
現在の文章：
| library(tidyverse) | |
| library(reticulate) | |
| read_raw_pkl <- function(file_path="/path/to/raw.pkl"){ | |
| # https://gist.github.com/kagaya/32706c38404c99431669ba34b8bccbac | |
| j <- import("joblib") # python package joblib is loaded via {reticulate} | |
| arm_xy <- j$load(file_path) | |
| tibble(t=1:length(arm_xy$path), path=arm_xy$path) %>% | |
| group_by(t) %>% |
| --- | |
| title: Supplementary information for the paper 'Individual behavioral type captured by a Bayesian model comparison in cap making by sponge crab' | |
| --- | |
| This document is the supplementary information for the paper 'Individual behavioral type captured by a Bayesian framework in cap making by sponge crab' by Harada, Hayashi and Kagaya. | |
| The basic conceptural framework for data analysis in the paper is a Bayesian framework based on the mathematical theory founded by Dr. Akaike and Dr. Watanabe (Watanabe, 'Mathematical Theory of Bayesian Statistics', 2018). The author of this document wrote an example of model selection by Widely Applicable Information Criterion (WAIC) using the data of 'artificial case' (http://rpubs.com/katzkagaya/460937). It may be of help for clarifying what the analysis of this document is doing, by using numerically generated 'artificial' data and several relatively simple hierarchical models. | |
| In this study, we deal with the behavioral data of a 'natural case'. Therefore, we do not know |
| --- | |
| title: "an example of model comparison by WAIC (draft)" | |
| author: "Katsushi Kagaya" | |
| date: "`r Sys.time()`" | |
| output: | |
| html_document: default | |
| --- | |
| # motivation and aims |
| --- | |
| header-includes: | |
| - \usepackage{setspace} | |
| - \doublespacing | |
| - \usepackage{lineno} | |
| - \linenumbers | |
| output: | |
| pdf_document: default | |
| html_document: | |
| df_print: paged |
| functions { | |
| vector my_student_t_rng(int N, real nu, real mu, real sigma){ | |
| vector[N] Y; | |
| for (n in 1:N){ | |
| Y[n] = student_t_rng(nu, mu, sigma); | |
| } | |
| return(Y); | |
| } | |
| vector my_ZIP_rng(int N, real q, real lambda){ |
| library(tidyverse) | |
| library(ggrepel) | |
| library(rstan) | |
| rstan::expose_stan_functions("my_stan_functions.stan") # https://gist.github.com/kagaya/c726f16d82b80026bb1924b408a72b5c | |
| source("utility.R") | |
| ###### choice ##### |
| waic <- function(log_likelihood) { | |
| # from https://gist.github.com/MatsuuraKentaro/3f6ae5863e700f5039c19e36a9bdf646 | |
| training_error <- - mean(log(colMeans(exp(log_likelihood)))) | |
| functional_variance_div_N <- mean(colMeans(log_likelihood^2) - colMeans(log_likelihood)^2) | |
| waic <- training_error + functional_variance_div_N | |
| return(waic) | |
| } |
| library(tidyverse) | |
| library(grid) | |
| library(gtable) | |
| plot_segment_across_facets <- function(p, from=1, to=2, | |
| from_point_id=1, | |
| to_point_id=1, | |
| plotout = F, | |
| gp=gpar(lty=1, alpha=0.5)){ |