Then resample the particles based on the weights at time k to obtain the forecast particles for the time points up to k.
This part is missing in the app. Corrected in the document
25 Matching Annotations
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preview-five-beta.vercel.app preview-five-beta.vercel.app
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for (i in 1:N) { likelihoods[i] <- likelihood(n, n_pos, predicted[i], method = "binomial") }
Can vectorize?
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- Aug 2025
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preview-five-beta.vercel.app preview-five-beta.vercel.app
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glm_mod
Keep only province for now
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c(0, 40, 60, 80, 100),
Use 60 as cutoff: 2 groups
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ICDbroad
Gout, proteinuria, KRT -> combine as Other
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preview.neurasense.io:30005 preview.neurasense.io:30005
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Figure 5. Comparison for total admission rate vs total admission number by year
Need better visualization?
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- Jul 2025
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preview-five-beta.vercel.app preview-five-beta.vercel.app
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Filter latest 6m donations
This was removed and instead, donations during the first 12m were removed
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- Jun 2025
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preview-five-beta.vercel.app preview-five-beta.vercel.app
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Males: Steady state
Do percentages
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posteriors on th
Get analytical for this
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The likelihood is approximated
This has a nice close form in normal measurement error. Should check if it is still possible with t-errors.
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for (s in 1:S) { bio_sims <- rnorm(K, theta_new_samples_pop[s], sigma_bio_samples[s, 1]) weights_k <- numeric(K) for (k in 1:K) { weights_k[k] <- exp(log(dt((y_new_1 - bio_sims[k]) / sigma_X_samples[s, 1], df = df_samples[s, 1] ) / sigma_X_samples[s, 1]) + log(dt((y_new_2 - bio_sims[k]) / sigma_X_samples[s, 1], df = df_samples[s, 1] ) / sigma_X_samples[s, 1])) } weights_steady[s] <- mean(weights_k, na.rm = T) }
something like: bio_sims <- rnorm((K, N), theta_new_samples, sigma_bio_samples) to sample a matrix of (K,N) normal random values and then plug that into dt(...) and then take the mean over the K axis of the matrix. Or you could at least vectorize the inner loop by first creating a vector of v_diff <- (y_new_1 - bio_sims)/sigma_X_samples and then do exp(log(dt(v_diff))... etc
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instantaneous true Hb and steady-state Hb
can do a heatmap of the difference?
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We have to estimate the likelihood
Can use a meta-model?
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preview-five-beta.vercel.app preview-five-beta.vercel.app
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It seems like the
Can do noise calibration. X axis: Cr region percentage; Y axis: Proportion of data points included and do eCDF type graph, then decide on the optimum.
Or simply, select 80% coverage in 80% interval or so
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ere is an add
add a dot at where resampling happens
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Author
This is a test annotation. Change to full name
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localhost:5501 localhost:5501
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neous true Hb values
can simplify taking the summaries from the draws
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localhost:5242 localhost:5242
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The likelihood is a
meta model? See if the diff in the two posterior using a heatmap
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localhost:6606 localhost:6606
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Interactive app
This is the version 2 of the app with different colors above and below the threshold.
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Local file Local file
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The state vector x is N dim
Test annotation
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localhost:3000 localhost:3000
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odel - conti
d
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Supun Manathunga
This is a test annotation
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- May 2025
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localhost:3000 localhost:3000
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Author
Annotaion "Author"
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preview-five-beta.vercel.app preview-five-beta.vercel.app
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Particle Filter
Test annotation
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localhost:5273 localhost:5273
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continuous
This is a test comment
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