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| library(MCMCpack) | |
| example_prior <- list(mean=3,sd=2) | |
| hyper_parameters <- list(alpha=1, beta=1) | |
| data_samples = rnorm(100,6,3) | |
| random_samples <- rnorm(100,20,5) | |
| find_likelyhood <- function(target_model, target) { | |
| return(dnorm(target,target_model$mean, target_model$sd)) | |
| } | |
| parameter_log_likelyhood <- function(data_samples, prior_model, target_model) { | |
| likely_hood = 0 | |
| log_prior = log((dnorm(target_model$mean, prior_model$mean, prior_model$sd))) | |
| for(index in 1:length(data_samples)) { | |
| sample <- find_likelyhood(target_model, data_samples[index]) | |
| likely_hood = log(sample) + likely_hood | |
| } | |
| return (likely_hood + log_prior) | |
| } | |
| new_sd_based_on_mean <- function(data_samples, target_model, hyper_parameter) { | |
| mean_sum = 0 | |
| for(index in 1:length(data_samples)) { | |
| mean_sum = mean_sum + (data_samples[index] - target_model$mean) | |
| } | |
| mean_sum = mean_sum /2 | |
| alpha = hyper_parameter$alpha + (length(data_samples)/2) | |
| beta = hyper_parameter$beta + mean_sum | |
| if(beta < 1) { | |
| target_model$sd = 1 | |
| target_model$mean = target_model$mean + target_model$sd | |
| return(target_model) | |
| } | |
| target_model$sd = (rinvgamma(1, alpha, beta)) | |
| target_model$mean = target_model$mean + target_model$sd | |
| return(target_model) | |
| } | |
| find_map <- function(data_samples, prior, hyper_parameters, iter=1000, max_jump = 5) { | |
| ret_val <- prior | |
| best_log = -999999 | |
| for(iteration in 1:iter) { | |
| permute <- ret_val | |
| #Change mean | |
| if(sample(0:1,1) == 0) { | |
| permute$mean = permute$mean - runif(1,0,max_jump) | |
| } else { | |
| permute$mean = permute$mean + runif(1,0,max_jump) | |
| } | |
| #Change sd based on new mean | |
| permute = new_sd_based_on_mean(data_samples, permute, hyper_parameters) | |
| if(permute$sd >= 0 && permute$mean >= 1) { | |
| current_log = parameter_log_likelyhood(data_samples, prior, permute) | |
| if(is.nan(as.numeric(current_log)) == FALSE) { | |
| if(current_log > best_log) { | |
| best_log = current_log | |
| ret_val = permute | |
| print(paste(paste(paste(paste(paste("New Attr Found (mean): ",permute$mean), " (sd): "), permute$sd), " Log: "), current_log)) | |
| } | |
| } | |
| } | |
| } | |
| return(ret_val) | |
| } | |
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