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Fix rendering of subheaders in docs #160

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Oct 10, 2023
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Fix rendering of subheaders in docs #160

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13d128b
fix headers in kf example
bartvanerp Oct 10, 2023
7e1429c
rm hyperlinks
bartvanerp Oct 10, 2023
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12 changes: 3 additions & 9 deletions examples/advanced_examples/Global Parameter Optimisation.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -13,12 +13,6 @@
"source": [
"This notebook demonstrates how to optimize parameters in state space models using external optimization packages, such as [Optim.jl](https://github.com/JuliaNLSolvers/Optim.jl/) and [Flux.jl](https://github.com/FluxML/Flux.jl). We utilize **RxInfer.jl**, a powerful package for inference in probabilistic models.\n",
"\n",
"[Section 1: Univariate State Space Model](#univariate-state-space-model)\n",
"\n",
"[Section 2: Multivariate State Space Model](#multivariate-state-space-model)\n",
"\n",
"[Section 3: Learning Kalman Filter with LSTM-Driven Dynamic](#learning-kalman-filter-with-lstm-driven-dynamic)\n",
"\n",
"By the end of this notebook, you will have practical knowledge of global parameter optimization in state space models. You will learn how to optimize parameters in both univariate and multivariate state space models, and harness the power of external optimization packages such as **Optim.jl** and **Flux.jl**."
]
},
Expand All @@ -44,7 +38,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Univariate State Space Model<a id='univariate-state-space-model'></a>\n",
"## Univariate State Space Model\n",
"\n",
"Let us try use the following simple state space model:\n",
"\n",
Expand Down Expand Up @@ -239,7 +233,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multivariate state space model <a id='multivariate-state-space-model'></a>"
"## Multivariate state space model"
]
},
{
Expand Down Expand Up @@ -744,7 +738,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning Kalman filter with LSTM driven dynamic<a id='learning-kalman-filter-with-lstm-driven-dynamic'></a>"
"## Learning Kalman filter with LSTM driven dynamic"
]
},
{
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16 changes: 2 additions & 14 deletions examples/basic_examples/Kalman filtering and smoothing.ipynb
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Expand Up @@ -54,24 +54,12 @@
"Utimately, we show how **RxInfer.jl** can deal with missing observations."
]
},
{
"cell_type": "markdown",
"id": "4d3fcfcb-ff7d-4c59-beda-8acd3a20b85d",
"metadata": {},
"source": [
"[Section 1: Gaussian Linear Dynamical System](#gaussian-linear-dynamical-system)\n",
"\n",
"[Section 2: Multivariate State Space Model](#multivariate-state-space-model)\n",
"\n",
"[Section 3: Handling Missing Data](#missing_data)\n"
]
},
{
"cell_type": "markdown",
"id": "aab4197e-4522-493f-8f5d-254140e6c326",
"metadata": {},
"source": [
"## Gaussian Linear Dynamical System <a id=\"gaussian-linear-dynamical-system\"></a>"
"## Gaussian Linear Dynamical System"
]
},
{
Expand Down Expand Up @@ -6614,7 +6602,7 @@
"id": "d9720084-d4a4-4928-bb3c-569a70a14d38",
"metadata": {},
"source": [
"### Handling Missing Data<a id='missing_data'></a>"
"## Handling Missing Data"
]
},
{
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